commit 82c49951c891f36dd6e1a4983edc8383dbd01da1
Author: ModelHub XC <noreply@modelhub.org.cn>
Date:   Fri Aug 21 19:45:13 2026 +0800

    初始化项目，由ModelHub XC社区提供模型
    
    Model: TIGER-Lab/FIM-14B
    Source: Original Platform

diff --git a/.gitattributes b/.gitattributes
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diff --git a/README.md b/README.md
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+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen2.5-Coder-14B-Instruct
+datasets:
+- TIGER-Lab/FIM-Midtraining-400K
+- R2E-Gym/R2EGym-SFT-Trajectories
+tags:
+- code
+- software-engineering
+- agent
+---
+
+# FIM-14B
+
+[📄 Paper](https://arxiv.org/abs/2607.12463) · [💻 GitHub](https://github.com/TIGER-AI-Lab/FIM-Midtraining) · [🤗 Dataset](https://huggingface.co/datasets/TIGER-Lab/FIM-Midtraining-400K) · [🤗 Collection](https://huggingface.co/collections/TIGER-Lab/fim-midtraining)
+
+**FIM-14B** is the 14B coding-agent model of *"Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models"*: `Qwen2.5-Coder-14B-Instruct`, mid-trained on function-aware FIM data, then post-trained on R2E-Gym agent trajectories with the upstream recipe unmodified. The mid-training stage is the only difference from a standard R2E-Gym reproduction — worth **+3.0 points on SWE-Bench-Verified and +4.0 on SWE-Bench-Lite**, while also recovering most of the general-capability erosion that agentic post-training inflicts (LiveCodeBench +11.1, τ-bench +3.9, BFCL +2.4 over the post-training-only arm).
+
+## Training pipeline
+
+- **Base model**: [`Qwen/Qwen2.5-Coder-14B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct)
+- **FIM mid-training**: [`midtraining/configs/fim_midtrain.yaml`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/blob/main/midtraining/configs/fim_midtrain.yaml) on [TIGER-Lab/FIM-Midtraining-400K](https://huggingface.co/datasets/TIGER-Lab/FIM-Midtraining-400K) (as-run copy: [`FIM_Midtrain_14B.yaml`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/blob/main/midtraining/configs/FIM_Midtrain_14B.yaml)) → intermediate checkpoint released as [TIGER-Lab/FIM-Mid-14B](https://huggingface.co/TIGER-Lab/FIM-Mid-14B)
+- **Post-training**: SFT on R2E-Gym agent trajectories — [`posttraining/r2egym/`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/tree/main/posttraining/r2egym) (as-run copy: [`FIM_Posttrain_14B.yaml`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/blob/main/posttraining/r2egym/FIM_Posttrain_14B.yaml))
+
+## Results
+
+Means over three evaluation seeds, identical harness for both arms (paper Tables 1–2):
+
+| Setting | SWE-Bench-Verified | SWE-Bench-Lite |
+|---|---|---|
+| Qwen2.5-Coder-14B-Instruct + R2E-Gym (reproduced) | 26.20 | 18.00 |
+| **FIM-14B (+ FIM mid-training)** | **29.20** | **22.00** |
+| Δ | +3.00 | +4.00 |
+
+Capability preservation at 14B (six benchmarks outside SWE-Bench):
+
+| Setting | LiveCode | OJBench | FSB-EN | Terminal | τ-bench | BFCL | Avg |
+|---|---|---|---|---|---|---|---|
+| + R2E-Gym only | 24.10 | 2.80 | 47.72 | 2.41 | 3.40 | 15.80 | 16.04 |
+| **FIM-14B** | **35.20** | **4.74** | **48.25** | **3.66** | **7.30** | **18.20** | **19.56** |
+
+Reproduction guides for all six: [`evaluation/`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/tree/main/evaluation).
+
+## Evaluate on SWE-Bench Verified
+
+FIM-14B is evaluated with the **R2E-Gym agent scaffold** (fixed by its post-training pipeline). The complete pinned walkthrough lives at [`evaluation/swebench/released_checkpoints.md`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/blob/main/evaluation/swebench/released_checkpoints.md).
+
+### 1. Serve the model with vLLM
+
+```bash
+CUDA_VISIBLE_DEVICES=0 \
+VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \
+python -m vllm.entrypoints.openai.api_server \
+  --model TIGER-Lab/FIM-14B \
+  --served-model-name FIM-14B \
+  --host 127.0.0.1 \
+  --port 8400 \
+  --tensor-parallel-size 1 \
+  --max-model-len 65536 \
+  --hf-overrides '{"max_position_embeddings": 65536}' \
+  --enable-prefix-caching \
+  --gpu-memory-utilization 0.9 \
+  > vllm_fim14b.log 2>&1 &
+```
+
+Wait until the server is up (model load takes ~1 minute):
+
+```bash
+curl -s http://127.0.0.1:8400/v1/models
+```
+
+### 2. Run the agent on SWE-Bench Verified
+
+From an upstream, unmodified [R2E-Gym](https://github.com/R2E-Gym/R2E-Gym) checkout (Docker required):
+
+```bash
+export OPENAI_API_KEY=EMPTY
+export LLM_BASE_URL="http://127.0.0.1:8400/v1"
+
+uv run python src/r2egym/agenthub/run/edit.py runagent_multiple \
+  --dataset "R2E-Gym/SWE-Bench-Verified" \
+  --split "test" \
+  --start_idx 0 \
+  --k 500 \
+  --traj_dir "./traj" \
+  --exp_name "FIM-14B_swebench_verified_r1" \
+  --llm_name "openai/FIM-14B" \
+  --scaffold "r2egym" \
+  --backend "docker" \
+  --use_fn_calling False \
+  --temperature 0 \
+  --max_steps 40 \
+  --max_steps_absolute 100 \
+  --max_workers 6 \
+  --max_reward_calc_time 1200 \
+  --max_tokens 65536 \
+  --use_existing True
+```
+
+For SWE-Bench Lite, use `--dataset "R2E-Gym/SWE-Bench-Lite" --k 300`.
+
+### 3. Score with the official SWE-bench harness
+
+Convert the trajectories to a submission and score with the official harness — [`evaluation/swebench/score.sh`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/blob/main/evaluation/swebench/score.sh). The reported number is `resolved_instances / total_instances`.
+
+## Citation
+
+```bibtex
+@article{wang2026fim,
+  title={Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models},
+  author={Wang, Yubo and Liang, Jiarong and Zhang, Yuxuan and Liu, Xuye and Wei, Cong and Zhang, Yuyu and Nie, Ping and Chen, Wenhu},
+  journal={arXiv preprint arXiv:2607.12463},
+  year={2026}
+}
+```
diff --git a/added_tokens.json b/added_tokens.json
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--- /dev/null
+++ b/added_tokens.json
@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
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+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}
diff --git a/chat_template.jinja b/chat_template.jinja
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--- /dev/null
+++ b/chat_template.jinja
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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}
diff --git a/config.json b/config.json
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--- /dev/null
+++ b/config.json
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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
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+    "full_attention",
+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 48,
+  "model_type": "qwen2",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.1",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}
diff --git a/configuration.json b/configuration.json
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--- /dev/null
+++ b/configuration.json
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diff --git a/generation_config.json b/generation_config.json
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--- /dev/null
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+{
+  "do_sample": true,
+  "eos_token_id": [
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+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.57.1"
+}
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