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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,114 @@
+LLAMA 3.1 COMMUNITY LICENSE AGREEMENT
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README.md

@@ -1,3 +1,233 @@
----
-license: llama3.1
----
+---
+language:
+- en
+- ja
+license: llama3.1
+pipeline_tag: text-generation
+model_type: llama
+datasets:
+- bigcode/the-stack-v2
+- bigcode/jupyter-code-text-pairs
+- bigcode/the-stack-github-issues
+tags:
+- llama-3
+- code
+---
+
+# Llama 3.1 Future Code Ja
+
+Llama 3.1 Future Code Ja is a large language model with 8B parameters built on top of the [Meta Llama 3.1](https://huggingface.co/meta-llama/Llama-3.1-8B) model.
+The model was first experienced continual pre-trained on the mixture of code and mostly-Japanese natural language data.
+The training data is mainly from [The Stack V2 dataset](bigcode/the-stack-v2) and the subset of [LLM-jp Corpus v3](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3), which comprises 204.9B code and 85.7B natural language tokens after carefully designed data cleaning.
+The model was then merged with the instruct variant of the Meta Llama 3.1 model to acquire abilities to follow general task instructions, followed by supervised fine-tuning (SFT) and direct preference optimization (DPO) on our own magpie-generated code instruction data.
+
+The model officially supports Japanese and English for natural languages and more than 40 programming languages ranging from popular Python, Java etc. to some legacy languages such as COBOL.
+In addition to causal (left-to-right) inference, the model supports Fill-in-the-Middle (FIM) capability, where the model fills in the blank attending to bidirectional context, a common use case in IDEs.
+
+The model outperforms the original Llama 3.1 model in both Japanese, and English-instructed code completion tasks in various programming languages, and outperforms Qwen families in Japanese generation tasks, attaining a good balance between specialty in code-related tasks and general ability in Japanese.
+
+## Usage
+
+Here are the sample inference scripts with transformers.
+We recommend using [vLLM](https://github.com/vllm-project/vllm) for faster inference.
+
+```bash
+pip install torch transformers accelerate
+```
+
+### Chat
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "future-architect/Llama-3.1-Future-Code-Ja-8B"
+
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# we recommend using the following system prompt:
+# for Japanese completion : "あなたは様々なソフトウェア開発タスクをサポートするAIアシスタントです。"
+# for English completion : "You are an AI assistant who support various software development tasks."
+
+message = [
+    {
+        "role": "system",
+        "content": "あなたは様々なソフトウェア開発タスクをサポートするAIアシスタントです。"
+    },
+    {
+        "role": "user",
+        "content": "PythonでFizzBuzzを書いてください。",
+    },
+]
+
+input_ids = tokenizer.apply_chat_template(
+    message, add_generation_prompt=True, return_tensors="pt", return_dict=True
+).to(model.device)
+
+output = model.generate(**input_ids, max_new_tokens=1024)
+
+print(tokenizer.decode(output[0, input_ids["input_ids"].shape[1]:]))
+```
+
+### Fill-in-the-Middle
+
+**With the idea that the users may not want line breaks just after their cursor positions, we did not create any middle splits that start with newline symbols (`\n`), but included them at the end of the prefix instead.**
+**This also holds true for the boundaries of suffix and middle splits, causing great sensitivity against which split to include newline symbols.**
+**Please remove one new line symbol (if exists) from the beginning of the suffix for improved performance.**
+
+You may set a larger repetition penalty to avoid nonsense generations with too many signs.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+FIM_PREFIX = "<|fim_prefix|>"
+FIM_MIDDLE = "<|fim_middle|>"
+FIM_SUFFIX = "<|fim_suffix|>"
+
+model_name = "future-architect/Llama-3.1-Future-Code-Ja-8B"
+
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# prepend <|begin_of_text|> to inform that this is the beginning of "the content" (not whole sequence with special tokens)
+prefix = "<|begin_of_text|>def fizzbuzz(n"
+suffix = "return n"
+
+# PSM mode (infilling)
+input_txt = FIM_PREFIX + prefix + FIM_SUFFIX + suffix + FIM_MIDDLE
+# SPM mode (reverse infilling)
+# input_txt = FIM_PREFIX + FIM_SUFFIX + suffix + FIM_MIDDLE + prefix
+
+# set add_special_tokens to False, so that the tokenizer does NOT add <|begin_of_text|> before special tokens
+input_ids = tokenizer(input_txt, add_special_tokens=False, return_tensors="pt").to(model.device)
+
+output = model.generate(**input_ids, max_new_tokens=1024, temperature=0.2, top_p=0.95)
+
+print(tokenizer.decode(output[0, input_ids["input_ids"].shape[1]:]))
+```
+
+## Model Performance
+
+### Code completion (Japanese)
+
+- [JHumanEval](https://huggingface.co/datasets/kogi-jwu/jhumaneval) (Sato et al., 2024)
+- [JMultiPL-E](https://huggingface.co/datasets/tohoku-nlp/JMultiPL-E) (Taneguchi et al., 2025)
+
+Note: We do not report scores for two programming languages (Julia and Racket), which we did not include in the training data. All the scores below are pass@1 with 10 trials.
+
+| model                    | size   |     py |    cpp |     cs |      d |     go |   java |     js |    php |     pl |      r |     rb |     rs |   scala |     sh |   swift |     ts |
+|--------------------------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|---------|--------|---------|--------|
+| Llama 3.1 Future Code Ja | 8B     | 0.6335 | 0.5267 | 0.3633 | 0.1564 | 0.6286 | 0.4696 | 0.5528 | 0.4814 | 0.2919 | 0.2969 | 0.1870 | 0.4487 |  0.4425 | 0.3285 |  0.3861 | 0.5623 |
+| Llama 3.1                | 8B     | 0.5061 | 0.4391 | 0.2835 | 0.2147 | 0.5519 | 0.3753 | 0.4640 | 0.4248 | 0.2584 | 0.2360 | 0.3112 | 0.3269 |  0.3175 | 0.2665 |  0.3323 | 0.4799 |
+| Llama 3.1 Swallow        | 8B     | 0.4213 | 0.3329 | 0.2456 | 0.1026 | 0.6370 | 0.3468 | 0.3112 | 0.3273 | 0.1758 | 0.1807 | 0.0503 | 0.2090 |  0.2487 | 0.1525 |  0.2354 | 0.3258 |
+| Qwen2.5                  | 7B     | 0.6018 | 0.5106 | 0.3601 | 0.2353 | 0.7500 | 0.5044 | 0.5416 | 0.5267 | 0.3075 | 0.3466 | 0.3683 | 0.5071 |  0.3969 | 0.3380 |  0.4576 | 0.6025 |
+| Qwen2.5-Coder            | 7B     | 0.6695 | 0.6379 | 0.4601 | 0.1660 | 0.7110 | 0.5468 | 0.6696 | 0.5894 | 0.3497 | 0.4174 | 0.3565 | 0.6032 |  0.4950 | 0.3544 |  0.5285 | 0.6358 |
+| Qwen3                    | 8B     | 0.6256 | 0.5683 | 0.3709 | 0.1583 | 0.5156 | 0.4778 | 0.5814 | 0.5547 | 0.3969 | 0.2466 | 0.3217 | 0.4763 |  0.4075 | 0.3418 |  0.3715 | 0.5239 |
+| Gemma 2                  | 9B     | 0.5549 | 0.4590 | 0.3608 | 0.0897 | 0.7052 | 0.4601 | 0.2863 | 0.4733 | 0.1099 | 0.1615 | 0.1205 | 0.3417 |  0.3850 | 0.1209 |  0.3272 | 0.2346 |
+
+### Code completion (English)
+
+- [HumanEval](https://huggingface.co/datasets/openai/openai_humaneval) (Chen et al., 2021)
+- [MultiPL-E](https://huggingface.co/datasets/nuprl/MultiPL-E) (Cassano et al., 2022)
+
+Note: We do not report scores for two programming languages (Julia and Racket), which we did not include in the training data. All the scores below are pass@1 with 10 trials.
+
+| model                    | size   |     py |    cpp |     cs |      d |     go |   java |     js |    php |     pl |      r |     rb |     rs |   scala |     sh |   swift |     ts |
+|--------------------------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|---------|--------|---------|--------|
+| Llama 3.1 Future Code Ja | 8B     | 0.6835 | 0.5795 | 0.3829 | 0.1692 | 0.6279 | 0.4987 | 0.6149 | 0.5565 | 0.3652 | 0.3317 | 0.1752 | 0.4846 |  0.4662 | 0.3595 |  0.4525 | 0.6390 |
+| Llama 3.1                | 8B     | 0.6311 | 0.4795 | 0.3184 | 0.2083 | 0.5909 | 0.4715 | 0.5571 | 0.4658 | 0.3236 | 0.2696 | 0.4267 | 0.3744 |  0.3856 | 0.2994 |  0.3741 | 0.5717 |
+| Llama 3.1 Swallow        | 8B     | 0.4701 | 0.3720 | 0.2646 | 0.1224 | 0.6519 | 0.3759 | 0.3006 | 0.3733 | 0.1752 | 0.1447 | 0.0590 | 0.2103 |  0.2744 | 0.1614 |  0.2190 | 0.3786 |
+| Qwen2.5                  | 7B     | 0.6732 | 0.5491 | 0.4253 | 0.2455 | 0.7000 | 0.6013 | 0.6137 | 0.5913 | 0.3373 | 0.3832 | 0.4429 | 0.5923 |  0.4263 | 0.3715 |  0.5095 | 0.6535 |
+| Qwen2.5-Coder            | 7B     | 0.7890 | 0.7373 | 0.5152 | 0.1936 | 0.3935 | 0.6184 | 0.7385 | 0.6528 | 0.3969 | 0.4224 | 0.4230 | 0.6545 |  0.5725 | 0.4158 |  0.5797 | 0.7434 |
+| Qwen3                    | 8B     | 0.7134 | 0.6702 | 0.4285 | 0.2295 | 0.4721 | 0.5747 | 0.6602 | 0.6236 | 0.4441 | 0.3627 | 0.4261 | 0.6154 |  0.5363 | 0.4089 |  0.4304 | 0.6082 |
+| Gemma 2                  | 9B     | 0.6128 | 0.5118 | 0.3728 | 0.1045 | 0.6552 | 0.4791 | 0.3758 | 0.4863 | 0.0783 | 0.1186 | 0.0795 | 0.3853 |  0.4162 | 0.1437 |  0.3506 | 0.3723 |
+
+### Fill-in-the-Middle
+
+- [SantaCoder-FIM](https://huggingface.co/datasets/bigcode/santacoder-fim-task) (Allal et al., 2023)
+
+Note: The models with asterisk (*) do not support FIM. We used the SPM prompt in [Gong et al., 2024](https://arxiv.org/pdf/2403.04814) and truncated the generated output just before the point that matched the beginning of the provided suffix. The scores of Llama models on PSM mode are not reported here since we got almost 0 scores for all those settings. All the scores below are exact match (EM) with 1 trial.
+
+| model                    | size   |   PSM (py) |   SPM (py) |   PSM (js) |   SPM (js) |   PSM (java) |   SPM (java) |
+|--------------------------|--------|------------|------------|------------|------------|--------------|--------------|
+| Llama 3.1 Future Code Ja | 8B     |     0.5216 |     0.5139 |     0.6018 |     0.6049 |       0.5517 |       0.5478 |
+| Qwen2.5-Coder            | 7B     |     0.5829 |     0.4084 |     0.6612 |     0.5597 |       0.6433 |       0.6180 |
+| Llama 3.1 8B *           | 8B     |     - |     0.4468 |     - |     0.3951 |       - |       0.3506 |
+| Llama 3.1 70B *          | 70B    |     - |     0.5964 |     - |     0.5084 |       - |       0.2910 |
+
+### Japanese tasks
+
+- JCommonSenseQA (Kurihara et al., 2022, Exact Match)
+- JEMHopQA (Ishii et al., 2024, chr-F1)
+- NIILC (Sekine, 2003, chr-F1)
+- JSQuAD (Kurihara et al., 2022, chr-F1)
+- XL-Sum (Hasan et al., 2021, ROUGE-2)
+- MGSM (Shi et al., 2023, Exact Match)
+- WMT20 en-ja (Barrault et al., 2020, BLEU)
+- WMT20 ja-en (Barrault et al., 2020, BLEU)
+
+| model                    | size   |   JCommonsenseQA |   JEMHopQA |   NIILC |   JSQuAD |   XL-SUM |   MGSM |   WMT20 en-ja |   WMT20 ja-en |
+|--------------------------|--------|------------------|------------|---------|----------|----------|--------|---------------|---------------|
+| Llama 3.1 Future Code Ja | 8B     |           0.9124 |     0.4983 |  0.5118 |   0.8758 |   0.1779 | 0.5480 |        0.2624 |        0.2028 |
+| Llama 3.1                | 8B     |           0.8829 |     0.4537 |  0.4050 |   0.8868 |   0.1486 | 0.5080 |        0.2195 |        0.2008 |
+| Llama 3.1 Swallow        | 8B     |           0.9240 |     0.5228 |  0.5805 |   0.8957 |   0.1920 | 0.5480 |        0.2818 |        0.2263 |
+| Qwen2.5                  | 7B     |           0.9142 |     0.4394 |  0.3998 |   0.8908 |   0.1690 | 0.6240 |        0.2091 |        0.1909 |
+| Qwen2.5-Coder            | 7B     |           0.8472 |     0.3014 |  0.3045 |   0.8906 |   0.1533 | 0.5360 |        0.1816 |        0.1598 |
+| Qwen3                    | 8B     |           0.9169 |     0.4265 |  0.4197 |   0.8943 |   0.1882 | 0.7720 |        0.2450 |        0.2133 |
+| Gemma 2                  | 9B     |           0.9312 |     0.5288 |  0.5306 |   0.8774 |   0.0873 | 0.4680 |        0.2305 |        0.2017 |
+
+### English tasks
+
+- TriviaQA (Joshi et al., 2017, Exact Match)
+- SQuAD2 (Rajpurkar et al., 2018, Exact Match)
+- GSM8K (Cobbe et al., 2021, Exact Match)
+
+| model                    | size   |   TriviaQA |   SQuAD2 |   GSM8K |
+|--------------------------|--------|------------|----------|---------|
+| Llama 3.1 Future Code Ja | 8B     |     0.6233 |   0.3754 |  0.7111 |
+| Llama 3.1                | 8B     |     0.6991 |   0.3784 |  0.7475 |
+| Llama 3.1 Swallow        | 8B     |     0.6296 |   0.3628 |  0.6126 |
+| Qwen2.5                  | 7B     |     0.5176 |   0.2624 |  0.7430 |
+| Qwen2.5-Coder            | 7B     |     0.4517 |   0.3388 |  0.7020 |
+| Qwen3                    | 8B     |     0.5631 |   0.3922 |  0.8749 |
+| Gemma 2                  | 9B     |     0.6573 |   0.3944 |  0.7908 |
+
+### Evaluation Details
+
+We used the [Code Generation LM Evaluation Harness](https://github.com/bigcode-project/bigcode-evaluation-harness) toolkit to evaluate code completion and FIM capabilities.
+
+We adopted the settings below for decoding.
+We mostly followed the recommendations however, we set `max_new_tokens` instead of `max_tokens` to avoid truncation while handling long input sequences.
+
+- Temperature: 0.2
+- Top-p: 0.95
+- Number of completions to generate: 10 (for completion tasks), 1 (for FIM tasks)
+- Maximum number of new tokens: 512
+
+We followed the evaluation strategy adopted in the Swallow project for Japanese and English tasks.
+More specifically, we used the [llm-jp-eval](https://github.com/llm-jp/llm-jp-eval) toolkit for Japanese tasks and the [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) toolkit for English (and some Japanese) tasks.
+
+We adopted the default decoding strategy for all the tasks.
+
+## Risks and Limitations
+
+The model is trained on general tasks related to software development, not on organization-specific, and/or non-standardized tasks.
+We recommend further fine-tuning the model to make it work better with those tasks.
+The model may produce incorrect output and all the suggestions from the model must be carefully examined before adopting in real-world applications.
+
+## Acknowledgements
+
+The model is developed as part of the Generative AI Accelerator Challenge (GENIAC) project.
+We thank great support from the New Energy and Industrial Technology Development Organization (NEDO) and the Ministry of Economy, Trade and Industry (METI) for financial support.
+
+## Contact
+
+- pj-geniac at future.co.jp
+
+## License
+
+[META LLAMA 3.1 COMMUNITY LICENSE](https://www.llama.com/llama3_1/license/)
+
+Copyright © 2025 by Future Corporation

config.json

@@ -0,0 +1,40 @@
+{
+  "_name_or_path": "future-architect/Llama-3.1-Future-Code-Ja-8B",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
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+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "extra_special_tokens": {},
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|finetune_right_pad_id|>",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}