Instructions to use Nanthasit/sakthai-context-7b-tools with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nanthasit/sakthai-context-7b-tools with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
model = PeftModel.from_pretrained(base_model, "Nanthasit/sakthai-context-7b-tools")

Transformers

How to use Nanthasit/sakthai-context-7b-tools with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Nanthasit/sakthai-context-7b-tools")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Nanthasit/sakthai-context-7b-tools", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nanthasit/sakthai-context-7b-tools with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nanthasit/sakthai-context-7b-tools"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanthasit/sakthai-context-7b-tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nanthasit/sakthai-context-7b-tools

SGLang

How to use Nanthasit/sakthai-context-7b-tools with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Nanthasit/sakthai-context-7b-tools" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanthasit/sakthai-context-7b-tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Nanthasit/sakthai-context-7b-tools" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanthasit/sakthai-context-7b-tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nanthasit/sakthai-context-7b-tools with Docker Model Runner:
```
docker model run hf.co/Nanthasit/sakthai-context-7b-tools
```

sakthai-context-7b-tools / README.md

Nanthasit

Fix model card: replace PEFT template with proper description

4c11b74 verified 8 days ago

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metadata

license: apache-2.0
language:
  - en
library_name: peft
pipeline_tag: text-generation
tags:
  - qwen2
  - lora
  - peft
  - sft
  - trl
  - transformers
  - sakthai
  - tool-calling
  - instruct
  - function-calling
  - text-generation
datasets:
  - Nanthasit/sakthai-combined-v5
base_model: Qwen/Qwen2.5-7B-Instruct

SakThai Context 7B — LoRA Adapter

A LoRA fine-tune of Qwen/Qwen2.5-7B-Instruct for structured tool-calling and instruction following, trained on the SakThai tool-calling curriculum.

Model Details

Developed by: Nanthasit
Base model: Qwen/Qwen2.5-7B-Instruct (7B parameters)
Architecture: Qwen2.5 decoder-only transformer + LoRA adapters
Fine-tuning method: LoRA (rank=16, alpha=32) via TRL SFTTrainer
Training data: Nanthasit/sakthai-combined-v5
License: Apache 2.0
Inference: BF16 (use transformers with device_map="auto")

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-7B-Instruct",
    torch_dtype="bfloat16",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")

model = PeftModel.from_pretrained(base_model, "Nanthasit/sakthai-context-7b-tools")

Chat Template

The model uses Qwen2.5's standard chat template with system/user/assistant roles and supports function-calling via the tools parameter in the tokenizer.

Merged Version

For production inference, use the merged model instead: 👉 Nanthasit/sakthai-context-7b-merged

Intended Use

Tool-calling and function-calling agents
Structured instruction following
Chat and assistant applications requiring external tool use

Training Details

Framework: Hugging Face TRL (SFTTrainer)
Compute: HF Jobs (T4 GPU)
Quantization: 4-bit NF4 for training
Dataset size: ~4,000+ tool-calling examples
LoRA config: r=16, lora_alpha=32, target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]