初始化项目，由ModelHub XC社区提供模型

Model: PursuitOfDataScience/llama3.2-1b-thinking
Source: Original Platform

.gitattributes

@@ -0,0 +1,36 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz 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

README.md

@@ -0,0 +1,212 @@
+---
+language:
+  - en
+license: other
+pipeline_tag: text-generation
+tags:
+  - llama
+  - chat
+  - sft
+  - reasoning
+  - cot
+  - ultrachat
+  - mixture-of-thoughts
+  - dpo
+base_model: meta-llama/Llama-3.2-1B
+library_name: transformers
+---
+
+# PursuitOfDataScience/llama3.2-1b-thinking
+
+This repository contains a three-stage fine-tuned version of **meta-llama/Llama-3.2-1B**:
+
+1. **Supervised fine-tuning (SFT)** on a local copy of **HuggingFaceH4/ultrachat_200k**
+   using an instruction-style, multi-turn chat objective.
+2. **Reasoning training** to enhance step-by-step reasoning capabilities,
+   building on the SFT model using the `open-r1/Mixture-of-Thoughts` dataset.
+3. **Direct Preference Optimization (DPO)** alignment using the `mlabonne/orpo-dpo-mix-40k` dataset
+   to improve response quality and alignment with human preferences.
+
+## Model details
+
+- **Base model**: `meta-llama/Llama-3.2-1B`
+- **Stage 1 objective**: Supervised fine-tuning for helpful, concise chat responses
+  on Ultrachat-style conversations.
+- **Stage 2 objective**: Specialized reasoning training to improve logical reasoning and
+  Chain of Thought (CoT) capabilities using step-by-step reasoning traces from `open-r1/Mixture-of-Thoughts`.
+- **Stage 3 objective**: DPO alignment to refine responses based on preference data from `mlabonne/orpo-dpo-mix-40k`,
+  enhancing safety, helpfulness, and adherence to user constraints.
+- **Context length**: Up to 131072 tokens (subject to the base model config).
+- **Training data**:
+  - SFT: multi-turn dialogues from `HuggingFaceH4/ultrachat_200k`.
+  - Reasoning: `open-r1/Mixture-of-Thoughts` dataset with step-by-step reasoning traces.
+  - DPO: preference pairs from `mlabonne/orpo-dpo-mix-40k`.
+
+## Inference usage
+
+The model is trained in a **chat-style** setup. At inference time, prompts are built
+as a list of `messages` and passed through the model's native `chat_template`
+via `tokenizer.apply_chat_template`:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+repo_id = "PursuitOfDataScience/llama3.2-1b-thinking"
+
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+model = AutoModelForCausalLM.from_pretrained(
+    repo_id,
+    device_map="auto",
+)
+
+messages = [
+    {
+        "role": "system",
+        "content": (
+            "You are a helpful, concise assistant. "
+            "Write clear, well-structured answers that follow the user's constraints."
+        ),
+    },
+    {
+        "role": "user",
+        "content": "Explain how someone can build a consistent daily learning habit.",
+    },
+]
+
+prompt_text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+
+inputs = tokenizer(prompt_text, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    eos_token_id=tokenizer.eos_token_id,
+    pad_token_id=tokenizer.eos_token_id,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True,
+)
+
+# Decode only the generated continuation (excluding the prompt tokens)
+generated_tokens = outputs[0][inputs["input_ids"].shape[1]:]
+response = tokenizer.decode(generated_tokens, skip_special_tokens=True)
+print(response)
+```
+
+### Multi-turn example
+
+```python
+messages = [
+    {
+        "role": "system",
+        "content": (
+            "You are a helpful, concise assistant. "
+            "Write clear, well-structured answers that follow the user's constraints."
+        ),
+    },
+    {
+        "role": "user",
+        "content": "Describe the main trade-offs between using small and large language models.",
+    },
+    {
+        "role": "assistant",
+        "content": "Small models are cheaper and faster, while large models are usually more capable...",
+    },
+    {
+        "role": "user",
+        "content": "Give me a bullet-point summary from the perspective of a startup.",
+    },
+]
+
+prompt_text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+
+inputs = tokenizer(prompt_text, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=256,
+    eos_token_id=tokenizer.eos_token_id,
+    pad_token_id=tokenizer.eos_token_id,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True,
+)
+response = tokenizer.decode(
+    outputs[0][inputs["input_ids"].shape[1]:],
+    skip_special_tokens=True,
+)
+print(response)
+```
+
+### Chain of Thought (CoT) reasoning example
+
+For reasoning tasks, the model can generate step-by-step thoughts using `<think>` tags:
+
+```python
+messages = [
+    {
+        "role": "system",
+        "content": (
+            "You are a helpful, concise assistant. "
+            "Use Chain of Thought reasoning with <think> tags for complex problems."
+        ),
+    },
+    {
+        "role": "user",
+        "content": "If a train travels 60 km in 1 hour, how long will it take to travel 180 km?",
+    },
+]
+
+prompt_text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+
+inputs = tokenizer(prompt_text, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    eos_token_id=tokenizer.eos_token_id,
+    pad_token_id=tokenizer.eos_token_id,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True,
+)
+response = tokenizer.decode(
+    outputs[0][inputs["input_ids"].shape[1]:],
+    skip_special_tokens=True,
+)
+print(response)
+# Example output: <think> The train travels 60 km in 1 hour, so speed is 60 km/h. For 180 km, time = distance / speed = 180 / 60 = 3 hours. </think> It will take 3 hours.
+```
+
+## Training pipeline (summary)
+
+1. **Instruction SFT (Ultrachat)**:
+   - Conversations are converted into lists of `messages`.
+   - For each assistant turn, a single training example is built using
+     `tokenizer.apply_chat_template`.
+   - Loss is applied only on assistant tokens; system and user tokens are masked.
+
+2. **Reasoning Training**:
+   - Fine-tuning on the `open-r1/Mixture-of-Thoughts` dataset with step-by-step reasoning traces to enhance CoT capabilities.
+   - Uses reinforcement learning or supervised methods to align with logical reasoning patterns.
+
+3. **DPO Alignment**:
+   - Fine-tuning with Direct Preference Optimization on the `mlabonne/orpo-dpo-mix-40k` dataset.
+   - Optimizes the model to prefer chosen responses over rejected ones, improving overall alignment.
+
+## Limitations
+
+- This is a relatively small (1B parameter) model and may hallucinate or
+  struggle on complex, multi-step reasoning tasks.
+- Outputs may be inaccurate, unsafe, or biased. Always verify critical
+  information before using it in production.

config.json

@@ -0,0 +1,36 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": 128001,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128001,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "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": true,
+  "transformers_version": "4.57.1",
+  "use_cache": false,
+  "vocab_size": 128256
+}

generation_config.json

@@ -0,0 +1,10 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001
+  ],
+  "pad_token_id": 128001,
+  "transformers_version": "4.57.1",
+  "use_cache": false
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c5996c8e2ffa1a18f668531c022df29eff74d17a97cccee0219fc88cfaecc3ae
+size 2471645608

special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
+size 17209920

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0ba6afbea00f01bf2c85893273c3ff67b7256f1487b806068ebf056259a3c1f9
+size 6456