初始化项目，由ModelHub XC社区提供模型

Model: ermiaazarkhalili/Llama-3.2-1B-Instruct_Function_Calling_xLAM
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 +1,219 @@
+---
+license: llama3.2
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - llama321binstruct
+  - sft
+  - fine-tuned
+  - trl
+  - lora
+  - text-generation
+  - conversational
+  - instruction-following
+base_model: meta-llama/Llama-3.2-1B-Instruct
+datasets:
+  - Salesforce/xlam-function-calling-60k
+model-index:
+  - name: Llama-3.2-1B-Function-Calling-xLAM
+    results: []
+---
+
+# Llama-3.2-1B-Function-Calling-xLAM
+
+This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) trained on the [Salesforce/xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) dataset using **SFT** with LoRA adapters.
+
+## Overview
+
+**Llama-3.2-1B-Function-Calling-xLAM** is a language model optimized using SFT. Supervised Fine-Tuning (SFT) trains the model to follow instructions by learning from high-quality demonstration data.
+
+### Key Features
+
+- **High-Quality Fine-Tuning**: Trained on N/A carefully curated examples
+- **Efficient Training**: Uses LoRA (Low-Rank Adaptation) with 4-bit quantization
+- **Strong Performance**: Achieves N/A token accuracy on evaluation set
+- **Optimized for Inference**: Available in multiple formats including GGUF quantizations
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| **Developed by** | [ermiaazarkhalili](https://huggingface.co/ermiaazarkhalili) |
+| **License** | LLAMA3.2 |
+| **Language** | English |
+| **Base Model** | [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) |
+| **Model Size** | 1B parameters |
+| **Tensor Type** | BF16 |
+| **Context Length** | 2,048 tokens |
+| **Training Method** | SFT with LoRA |
+
+## Training Information
+
+### Training Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| Learning Rate | 0.0002 |
+| Batch Size | 2 per device |
+| Gradient Accumulation Steps | 8 |
+| Effective Batch Size | 16 |
+| Number of Epochs | 1 |
+| Max Sequence Length | 2,048 tokens |
+| LR Scheduler | Linear warmup + Cosine annealing |
+| Warmup Ratio | 0.1 |
+| Precision | BF16 mixed precision |
+| Gradient Checkpointing | Enabled |
+| Random Seed | 42 |
+
+### LoRA Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| LoRA Rank (r) | 64 |
+| LoRA Alpha | 128 |
+| LoRA Dropout | 0.05 |
+| Target Modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| Quantization | 4-bit NF4 |
+
+### Training Metrics
+
+| Metric | Value |
+|--------|-------|
+| Hardware | NVIDIA H100 MIG |
+
+## Dataset
+
+This model was trained on the [Salesforce/xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) dataset.
+
+| Split | Samples |
+|-------|---------|
+| Training | N/A |
+| Evaluation | N/A |
+
+## Usage
+
+### Quick Start
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "ermiaazarkhalili/Llama-3.2-1B-Function-Calling-xLAM"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "What is the sum of 2 + 2?"}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
+response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
+print(response)
+```
+
+### Using Pipeline
+
+```python
+from transformers import pipeline
+
+generator = pipeline("text-generation", model="ermiaazarkhalili/Llama-3.2-1B-Function-Calling-xLAM", device_map="auto")
+messages = [{"role": "user", "content": "Explain the concept of machine learning."}]
+output = generator(messages, max_new_tokens=256, return_full_text=False)
+print(output[0]["generated_text"])
+```
+
+### 4-bit Quantized Inference
+
+```python
+from transformers import AutoModelForCausalLM, BitsAndBytesConfig
+import torch
+
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4"
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "ermiaazarkhalili/Llama-3.2-1B-Function-Calling-xLAM",
+    quantization_config=quantization_config,
+    device_map="auto"
+)
+```
+
+## GGUF Versions
+
+For CPU or mixed CPU/GPU inference, GGUF quantized versions are available at:
+[ermiaazarkhalili/Llama-3.2-1B-Function-Calling-xLAM-GGUF](https://huggingface.co/ermiaazarkhalili/Llama-3.2-1B-Function-Calling-xLAM-GGUF)
+
+### Using with Ollama
+
+```bash
+ollama pull hf.co/ermiaazarkhalili/Llama-3.2-1B-Function-Calling-xLAM-GGUF:Q4_K_M
+ollama run hf.co/ermiaazarkhalili/Llama-3.2-1B-Function-Calling-xLAM-GGUF:Q4_K_M "Hello!"
+```
+
+## Limitations
+
+- **Language**: Primarily trained on English data
+- **Knowledge Cutoff**: Limited to base model's training data cutoff
+- **Hallucinations**: May generate plausible-sounding but incorrect information
+- **Context Length**: Fine-tuned with 2,048 token limit
+- **Safety**: Not extensively safety-tuned; use with appropriate guardrails
+
+## Intended Use
+
+### Recommended Uses
+- Research on language model fine-tuning
+- Educational purposes
+- Personal projects
+- Prototyping conversational AI
+
+### Out-of-Scope Uses
+- Production systems without additional safety measures
+- Medical, legal, or financial advice
+- Generating harmful or misleading content
+
+## Training Framework
+
+- **TRL**: 0.24.0
+- **Transformers**: 4.57.3
+- **PyTorch**: 2.9.0
+- **Datasets**: 4.3.0
+- **PEFT**: 0.18.0
+- **BitsAndBytes**: 0.49.0
+
+## Citation
+
+```bibtex
+@misc{ermiaazarkhalili_llama_3.2_1b_function_calling_xlam,
+    author = {ermiaazarkhalili},
+    title = {Llama-3.2-1B-Function-Calling-xLAM: Fine-tuned Llama-3.2-1B-Instruct on xlam-function-calling-60k},
+    year = {2026},
+    publisher = {Hugging Face},
+    howpublished = {\url{https://huggingface.co/ermiaazarkhalili/Llama-3.2-1B-Function-Calling-xLAM}}
+}
+```
+
+## Acknowledgments
+
+- Base model developers at meta-llama
+- [Hugging Face TRL Team](https://github.com/huggingface/trl) for the training library
+- Dataset creators and contributors
+- Compute Canada / DRAC for HPC resources
+
+## Contact
+
+For questions or issues, please open an issue on the model repository.

chat_template.jinja

@@ -0,0 +1,93 @@
+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- if strftime_now is defined %}
+        {%- set date_string = strftime_now("%d %b %Y") %}
+    {%- else %}
+        {%- set date_string = "26 Jul 2024" %}
+    {%- endif %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+        {{- '{"name": "' + tool_call.name + '", ' }}
+        {{- '"parameters": ' }}
+        {{- tool_call.arguments | tojson }}
+        {{- "}" }}
+        {{- "<|eot_id|>" }}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,39 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "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,
+  "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,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.53.2",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

@@ -0,0 +1,12 @@
+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.53.2"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:177bf910e53e884e7fb784fd10283320c3fa6585cd10f334e06fdf28a009c03a
+size 2471645608

special_tokens_map.json

@@ -0,0 +1,16 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "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