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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nanthasit/sakthai-context-7b-merged")
model = AutoModelForCausalLM.from_pretrained("Nanthasit/sakthai-context-7b-merged", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Nanthasit/sakthai-context-7b-merged with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Nanthasit/sakthai-context-7b-merged:Q8_0
# Run inference directly in the terminal:
llama cli -hf Nanthasit/sakthai-context-7b-merged:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Nanthasit/sakthai-context-7b-merged:Q8_0
# Run inference directly in the terminal:
llama cli -hf Nanthasit/sakthai-context-7b-merged:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Nanthasit/sakthai-context-7b-merged:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Nanthasit/sakthai-context-7b-merged:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Nanthasit/sakthai-context-7b-merged:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nanthasit/sakthai-context-7b-merged:Q8_0

Use Docker

docker model run hf.co/Nanthasit/sakthai-context-7b-merged:Q8_0

LM Studio
Jan

vLLM

How to use Nanthasit/sakthai-context-7b-merged 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-merged"
# 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-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nanthasit/sakthai-context-7b-merged:Q8_0

SGLang

How to use Nanthasit/sakthai-context-7b-merged 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-merged" \
    --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-merged",
		"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-merged" \
        --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-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Nanthasit/sakthai-context-7b-merged with Ollama:
```
ollama run hf.co/Nanthasit/sakthai-context-7b-merged:Q8_0
```

Unsloth Studio

How to use Nanthasit/sakthai-context-7b-merged with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Nanthasit/sakthai-context-7b-merged to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Nanthasit/sakthai-context-7b-merged to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Nanthasit/sakthai-context-7b-merged to start chatting

How to use Nanthasit/sakthai-context-7b-merged with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nanthasit/sakthai-context-7b-merged:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Nanthasit/sakthai-context-7b-merged:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Nanthasit/sakthai-context-7b-merged with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nanthasit/sakthai-context-7b-merged:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Nanthasit/sakthai-context-7b-merged:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Nanthasit/sakthai-context-7b-merged with Docker Model Runner:
```
docker model run hf.co/Nanthasit/sakthai-context-7b-merged:Q8_0
```

Lemonade

How to use Nanthasit/sakthai-context-7b-merged with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nanthasit/sakthai-context-7b-merged:Q8_0

Run and chat with the model

lemonade run user.sakthai-context-7b-merged-Q8_0

List all available models

lemonade list

Hermes Agent

How to use Nanthasit/sakthai-context-7b-merged with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nanthasit/sakthai-context-7b-merged:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Nanthasit/sakthai-context-7b-merged:Q8_0

Run Hermes

hermes

Atomic Chat

Benchmark Results

Benchmark: sakthai-bench-v2 · 500 samples · run 2026-08-01

Overall (strict): 38.78 · Selection: 38.78 · Arguments: 45.53

Category	Count	Selection	Arguments	Strict
irrelevance_no_tools	50	100.00	100.00	100.00
irrelevance_tools	150	79.33	100.00	79.33
parallel	137	0.00	0.00	0.00
simple	122	7.38	7.38	7.38
held_out	-	10.71	10.71	10.71

Model Description

SakThai Context 7B Merged is a merged/continued checkpoint from the SakThai context family. It combines tool-use behaviour from Nanthasit/sakthai-context-7b-tools with long-context capability from Nanthasit/sakthai-context-7b-128k, continuing from Qwen/Qwen2.5-7B-Instruct.

It is intended for:

Open-ended text generation with strong local execution
Tool-calling and function-calling prompts
Long-context agent-style task completion with structured outputs
Offline CPU/edge deployment via llama.cpp / Ollama

Intended Use

Text generation and chat for English use cases
Tool-use workflows with <tools> XML prompt formatting
Research on small-to-mid tool-calling models under MIT license

How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Nanthasit/sakthai-context-7b-merged"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [
  {"role": "user", "content": "List your available tools first, then find today's weather in Bangkok."}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(out[0], skip_special_tokens=True))

For GGUF inference:

from llama_cpp import Llama

llm = Llama(model_path="sakthai-context-7b-merged.Q4_K_M.gguf", n_ctx=32768, n_threads=8)
out = llm("<|user|>\nWhat tools do you have?\n<|assistant|>\n", max_tokens=256)
print(out["choices"][0]["text"])

Datasets

This line was trained/evaluated against SakThai tool-use corpora:

Nanthasit/sakthai-combined-v6
Nanthasit/sakthai-combined-v7
Nanthasit/sakthai-combined-v8

Merged From

Nanthasit/sakthai-context-7b-tools
Nanthasit/sakthai-context-7b-128k

Benchmarks

Evaluation artifacts are preserved under .eval_results/ in this repository. Baseline comparisons should reference sibling cards:

Nanthasit/sakthai-context-7b-tools
Nanthasit/sakthai-context-7b-128k

Limitations

Merge/continued checkpoint; verify behavior before production use.
Tool calling performance depends on prompt formatting; prefer <tools> XML blocks.
Long-context generations are most reliable up to ~8k–32k tokens with appropriate context windows.
Benchmarking against held-out tools is encouraged before deployment.

Training

This checkpoint continues from prior SakThai context model merges. Datasets used earlier in the lineage include:

Nanthasit/sakthai-combined-v6
Nanthasit/sakthai-combined-v7
Nanthasit/sakthai-combined-v8

See eval/ and .eval_results/ in this repo for local evaluation artifacts.

Citation

@misc{sakthai-context-7b-merged,
  title = {SakThai Context 7B Merged},
  author = {Nanthasit (Beer)},
  year = {2026},
  url = {https://huggingface.co/Nanthasit/sakthai-context-7b-merged}
}

License

MIT

Downloads last month: 772

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Nanthasit/sakthai-context-7b-merged

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

(380)

this model

Datasets used to train Nanthasit/sakthai-context-7b-merged

Space using Nanthasit/sakthai-context-7b-merged 1

Collections including Nanthasit/sakthai-context-7b-merged

SakThai Context Models

Collection

All SakThai context fine-tunes: 0.5B to 7B, merged + adapters, 128K context, code variant. Built on Qwen2.5. • 8 items • Updated 14 days ago

SakThai Model Family

Collection

SakThai family: 47 curated items (26 models, 15 datasets, 6 spaces). • 35 items • Updated 1 day ago

Evaluation results

Selection Accuracy on SakThai Bench v2 (500 rows, scorer multiset-selection-v2)
self-reported

56.400
Arguments Accuracy on SakThai Bench v2 (500 rows, scorer multiset-selection-v2)
self-reported

12.300
Strict Accuracy on SakThai Bench v2 (500 rows, scorer multiset-selection-v2)
self-reported

12.300
Held-Out Tool Accuracy on SakThai Bench v2 (500 rows, scorer multiset-selection-v2)
self-reported

53.700
Degenerate Outputs on SakThai Bench v2 (500 rows, scorer multiset-selection-v2)
self-reported

0.000