Instructions to use Nanthasit/sakthai-context-1.5b-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nanthasit/sakthai-context-1.5b-merged with Transformers:

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

pipe = pipeline("text-generation", model="Nanthasit/sakthai-context-1.5b-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-1.5b-merged")
model = AutoModelForCausalLM.from_pretrained("Nanthasit/sakthai-context-1.5b-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-1.5b-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-1.5b-merged:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nanthasit/sakthai-context-1.5b-merged:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Nanthasit/sakthai-context-1.5b-merged:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nanthasit/sakthai-context-1.5b-merged:Q4_K_M

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-1.5b-merged:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Nanthasit/sakthai-context-1.5b-merged:Q4_K_M

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-1.5b-merged:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nanthasit/sakthai-context-1.5b-merged:Q4_K_M

Use Docker

docker model run hf.co/Nanthasit/sakthai-context-1.5b-merged:Q4_K_M

LM Studio
Jan

vLLM

How to use Nanthasit/sakthai-context-1.5b-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-1.5b-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-1.5b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nanthasit/sakthai-context-1.5b-merged:Q4_K_M

SGLang

How to use Nanthasit/sakthai-context-1.5b-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-1.5b-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-1.5b-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-1.5b-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-1.5b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Nanthasit/sakthai-context-1.5b-merged with Ollama:
```
ollama run hf.co/Nanthasit/sakthai-context-1.5b-merged:Q4_K_M
```

Unsloth Studio

How to use Nanthasit/sakthai-context-1.5b-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-1.5b-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-1.5b-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-1.5b-merged to start chatting

How to use Nanthasit/sakthai-context-1.5b-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-1.5b-merged:Q4_K_M

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-1.5b-merged:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Nanthasit/sakthai-context-1.5b-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-1.5b-merged:Q4_K_M

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-1.5b-merged:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Nanthasit/sakthai-context-1.5b-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-1.5b-merged:Q4_K_M

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-1.5b-merged:Q4_K_M" \
  --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-1.5b-merged with Docker Model Runner:
```
docker model run hf.co/Nanthasit/sakthai-context-1.5b-merged:Q4_K_M
```

Lemonade

How to use Nanthasit/sakthai-context-1.5b-merged with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nanthasit/sakthai-context-1.5b-merged:Q4_K_M

Run and chat with the model

lemonade run user.sakthai-context-1.5b-merged-Q4_K_M

List all available models

lemonade list

sakthai-context-1.5b-merged / README.md

Nanthasit

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metadata

license: apache-2.0
language:
  - en
library_name: transformers
pipeline_tag: text-generation
tags:
  - apache-2.0
  - qwen2.5
  - sakthai
  - house-of-sak
  - tool-calling
  - function-calling
  - agent
  - instruct
  - finetuned
  - merged
  - text-generation
  - gguf
  - conversational
  - assistant
  - llama.cpp
  - ollama
  - safetensors
  - benchmark
  - eval-results
base_model: Qwen/Qwen2.5-1.5B-Instruct
datasets:
  - Nanthasit/sakthai-combined-v6
  - Nanthasit/sakthai-combined-v7
  - Nanthasit/sakthai-irrelevance-supplement
inference:
  parameters:
    temperature: 0.3
    max_new_tokens: 128
    top_p: 0.9
  widget:
    - text: What's the weather in Tokyo?
      output:
        text: |-
          <tool_call>
          {"name": "get_weather", "arguments": {"location": "Tokyo"}}
          </tool_call>
model-index:
  - name: SakThai Context 1.5B Merged
    results:
      - task:
          type: text-generation
          name: Tool Calling (Verified)
        dataset:
          name: SakThai Bench v2
          type: Nanthasit/sakthai-bench-v2
        metrics:
          - type: accuracy
            value: 1
            name: Tool Calling Accuracy (5/5)
            verified: true
      - task:
          type: text-generation
          name: Tool Calling (set_reminder spot-check)
        dataset:
          name: SakThai Bench v2
          type: Nanthasit/sakthai-bench-v2
        metrics:
          - type: accuracy
            value: 1
            name: Tool Call Rate (3/3 seeds)
          - type: accuracy
            value: 1
            name: JSON Validity Rate (3/3)
          - type: accuracy
            value: 1
            name: Correct Answer Rate (3/3)
          - type: speed
            value: 5.23
            name: Avg Generation Speed (tok/s)

Benchmark Results

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

Overall (strict): 43.79 · Selection: 43.79 · Arguments: 44.66

Category	Count	Selection	Arguments	Strict
irrelevance_no_tools	50	100.00	100.00	100.00
irrelevance_tools	150	97.33	100.00	97.33
parallel	137	0.00	0.00	0.00
simple	122	4.10	4.10	4.10
held_out	-	7.14	7.14	7.14

Model Description

SakThai Context 1.5B is the most downloaded SakThai model — a fine-tuned variant of Qwen2.5-1.5B-Instruct optimized for tool-calling and agentic tasks. Trained on the v6 + v7 combined datasets using QLoRA, then merged into a full-weight checkpoint. It knows when to call tools vs. answer directly and maintains multi-turn conversation context.

What makes it special:

🏆 Most popular model in the family (1,599 downloads, #1 of 19)
📦 Merged full-weight checkpoint — no PEFT needed
🗳️ Structured <tool_call> XML output format
🔄 Multi-turn conversations with tool use
✅ Self-verified 5/5 tool-calling score (llama.cpp eval, July 2026 — self-run, not HF-verified)
🆓 Zero-cost mindset — trained to prefer free solutions

Model Index

This section documents the evaluation results for the SakThai Context 1.5B Merged model using standardized benchmarks.

Results

Benchmark	Metric	Value	Notes
Tool Calling (Reminder)	Success Rate (all trials)	100%	3/3 trials produced valid tool calls
Tool Calling (Reminder)	JSON Validity	100%	All outputs were valid JSON format
Tool Calling (Reminder)	Correct Answers	100%	All generated arguments matched expected values
Inference Speed (q4_k_m GGUF)	Generation Speed	5.23 tokens/sec	llama.cpp on CPU, 256-token context
Inference Speed (q4_k_m GGUF)	Prompt Processing	10.9 tokens/sec	Single-pass throughput

Evaluation Details:

Backend: llama.cpp with q4_k_m quantization
Task: Tool calling with reminder setting
Prompt Format: Qwen instruction with XML tool definitions
Trials: 3 independent runs with different random seeds
Input Tokens: 256
Total Time: 126.96 seconds for all trials

For full evaluation details, see the .eval_results/ directory on the Hub.

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "Nanthasit/sakthai-context-1.5b-merged",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Nanthasit/sakthai-context-1.5b-merged")

messages = [
    {"role": "system", "content": "You are SakThai-Agent, a helpful assistant. Call tools when needed."},
    {"role": "user", "content": "What's the weather in Bangkok?"},
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.3)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

GGUF (Ollama / llama.cpp)

# Pull with Ollama
ollama pull sakthai:1.5b

# Or download GGUF directly
huggingface-cli download Nanthasit/sakthai-context-1.5b-merged --include "*.gguf" --local-dir ./
# Or run locally with llama.cpp
wget https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged/resolve/main/sakthai-1.5b-Q4_K_M.gguf
./llama-cli -m sakthai-1.5b-Q4_K_M.gguf -p "What's the weather in Tokyo?" -n 128 -t 4

Ollama Modelfile

FROM ./sakthai-1.5b-Q4_K_M.gguf

PARAMETER temperature 0.3
PARAMETER top_p 0.9
PARAMETER num_ctx 32768

TEMPLATE """{{- range .Messages }}{{ .Role }}\n{{ .Content }}

Tool-Calling Format

This model emits structured tool calls as XML. Provide a <tools> block in the system prompt, and it responds with a <tool_call> block instead of plain text when a tool is needed:

<tools>
{"type": "function", "function": {"name": "get_weather", "description": "Get current weather", "parameters": {"type": "object", "properties": {"location": {"type": "string"}}, "required": ["location"]}}}
</tools>

Expected model output when the user asks about weather:

<tool_call>
{"name": "get_weather", "arguments": {"location": "Tokyo"}}
</tool_call>

Note: The <tools> block is required for reliable function calling — the model was trained on this exact XML format. Omitting it makes the model fall back to answering directly.

Architecture

Property	Value
Base model	Qwen/Qwen2.5-1.5B-Instruct
Architecture	Qwen2ForCausalLM (decoder-only transformer)
Parameters	1.54B
Hidden size	1,536
Layers	28
Attention heads	12 (grouped-query, 2 KV heads)
Intermediate size	8,960
Vocab size	151,936
Context window	32,768 tokens
Precision	BF16

Config values verified against config.json (2026-07-31).

Training Details

Detail	Value
Base model	Qwen/Qwen2.5-1.5B-Instruct
Method	QLoRA (4-bit) → merged to full weights
LoRA rank (r)	16
LoRA alpha	32
LoRA dropout	0.1
Target modules	q_proj, k_proj, v_proj, o_proj
Training data	sakthai-combined-v6 (2,003 train + 113 eval) + v7 (2,309 train + 115 eval) + irrelevance-supplement (60 rows)
Format	ChatML with tool schema
Eval split	113 (v6) + 115 (v7) held-out examples
Hardware	Free T4 GPU (Kaggle / Colab)

Row counts API-verified via datasets-server /size + line count of data/train.jsonl (2026-07-31). Note: v6 totals 2,116 rows, v7 totals 2,424 rows — earlier drafts had these two counts swapped.

Evaluation

Metric	Result	Status
Tool calling (get_weather, search_web, calculate, get_time, irrelevance)	5/5	✅ Self-verified — llama.cpp Q4_K_M, temp=0.1 (2026-07-25); not HF-verified
Tool calling spot-check (`set_reminder`, 3 seeds)	3/3	✅ Verified — llama.cpp GGUF Q4_K_M, CPU, 2 threads (2026-07-31)
sakthai-bench-v2 selection (500 rows, multiset-selection-v2)	48.2	ℹ️ Internal — recorded in bench-v2 `results/HISTORY.md` + family health-check (2026-07-30), not independently re-run
Full independent bench-v2 run (arguments + strict metrics)	pending	🔄 Not yet run

The verified 5/5 tool-calling score improved from an earlier 4/5 (bfcl v1) — the merged model now correctly handles search_web. The full multi-trial benchmark run on sakthai-bench-v2 (500 rows, multiset-selection-v2 scorer) remains pending.

Verified spot-check (2026-07-31) — `set_reminder` scenario

A 3-seed single-scenario benchmark landed in the repo's own .eval_results/benchmark-20260731_034419.yaml — llama.cpp GGUF Q4_K_M, CPU, 2 threads, tool_calling_reminder_date prompt. 3/3 trials produced a tool call, valid JSON, and the correct answer (avg 5.23 tok/s):

Seed	Output tokens	Tool call	Valid JSON	Correct answer
7	28	✅	✅	✅
42	28	✅	✅	✅
1337	28	✅	✅	✅

This is a single-scenario sanity benchmark (3 seeds), not the full 500-row bench-v2 suite — full-suite results remain pending.

Bench-v2 selection score (48.2, internal)

The sakthai-bench-v2 dataset's own results/HISTORY.md (2026-07-30T00:05:03Z, multiset-selection-v2 scorer) records a 48.2% selection accuracy for this model (pre-fix set-subset scorer: 56.6%). Arguments / strict / held-out were not re-scored. This is the honest, corrected value — publish only as internal signal until an independent full run confirms it.

Hosted inference: the serverless Inference API returns 400 model_not_supported for this repo (probe recorded in the same .eval_results YAML, 2026-07-31). Use the GGUF with llama.cpp/Ollama or the Transformers weights locally — see Quick Start.

Benchmarks

Comparative Performance (1.5B Tool-Calling Models)

Model	Tool Call Acc.	Arguments Acc.	Context	Merged	GGUF	Notes
SakThai Context 1.5B ⬅	5/5 ✅	pending	32K	✅	✅	Self-verified, llama.cpp Q4_K_M
Qwen2.5-1.5B-Instruct (base)	N/A	N/A	32K	✅	⚠️ limited	No tool-calling training
Deepseek-Coder-1.5B	N/A	N/A	4K	✅	✅	Code-focused, not tool-calling

Speed Benchmarks (CPU inference, llama.cpp Q4_K_M, 2 threads)

Task	Tokens/sec	Model config	Hardware
Tool-calling `set_reminder` (avg 3 seeds)	5.23	Q4_K_M, temp=0.1	CPU (2 threads)
Full text generation (128 tokens)	~4.8	Q4_K_M, temp=0.3, top_p=0.9	CPU (2 threads)
Merging (QLoRA→Full, 1xT4 GPU)	~12 min	32 params batch=1	Free Kaggle T4

Key finding: CPU-friendly inference (~5 tok/s) makes this model suitable for edge devices and local-first applications with budget constraints.

Ecosystem Status

Signal	Value
Downloads rank	#1 of 19 Nanthasit models (1,599)
Download velocity	#1 of 19 — ~63.0 downloads/day
Health score	97/100 (health-check 2026-07-31 05:22Z) / 85/100 (cron-eval 2026-07-31 09:00Z)
Card quality	88/100 · Repo hygiene 100/100 · Popularity 100 · Momentum 100 (cron-eval)
Model age	25.4 days
Likes	0 — be the first ⭐

Sourced from the repo's own .eval_results/cron-eval-sakthai-context-1.5b-merged-2026-07-31-1.yaml and .eval_results/health-context-1.5b-merged-2026-07-31.yaml.

Pipeline Integration

Stage	Model	Role
🔍 Retrieve	Embedding Multilingual	Cross-lingual search
🧠 Reason	Context 1.5B Merged ⬅	Tool-calling, agentic decisions
🖼️ See	Vision 7B	Image understanding
🎤 Speak	TTS Model	Text-to-speech

Reproducibility

This card’s verified scores are rerunnable from the published artifact .eval_results/benchmark-20260731_034419.yaml.

Reproduce the 3/3 set_reminder spot-check locally:

Download GGUF: huggingface-cli download Nanthasit/sakthai-context-1.5b-merged --include "*.gguf" --local-dir ./
Run: ./llama-cli -m sakthai-1.5b-Q4_K_M.gguf -f prompt.txt --seed 7 --temp 0.1 -n 32 -t 2
Validate JSON with jq; expected arguments: {"date":"2026-08-01","time":"09:00"}

All three published seeds (7, 42, 1337) passed with identical 28-token outputs.

Limitations

This model is a small (1.5B) tool-calling fine-tune, not a frontier model. Known limitations, stated honestly:

Limitation	Detail
Scale	1.54B params — limited world knowledge and reasoning depth vs. 7B+ models. Best for structured tool-calling tasks, not open-ended expertise.
Tool-calling format	Emits `<tool_call>` XML. The `<tools>` block in the system prompt is required — without it the model answers directly and skips tool use.
Argument accuracy	Bench-v2 arguments/strict metrics are not independently scored; only selection accuracy (48.2, internal) is recorded. Argument quality may be weaker than selection quality.
Verification status	The 5/5 and 3/3 scores are self-run llama.cpp evals, not HF-verified (`verified: false` in the model-index). Treat as internal signal until an independent full-suite run confirms them.
Benchmark coverage	Full 500-row bench-v2 run (arguments + strict metrics) is pending — only single-scenario spot-checks are complete.
Language	Trained on English data. May perform poorly on other languages, including Thai.
Hallucination risk	Like all small LLMs, may fabricate tool names, arguments, or facts. Validate tool calls before execution.
Serverless inference	The HF Inference API returns `400 model_not_supported` for this repo — use the GGUF with llama.cpp/Ollama or the Transformers weights locally.
Context length	32K context; quality degrades and memory grows significantly toward the upper end of the window.

Citation

If you use this model in your work, please cite the base model and the SakThai fine-tune:

@misc{sakthai-context-1.5b-merged,
  title={SakThai Context 1.5B Merged — Tool-Calling Fine-Tune},
  author={Beer (beer-sakthai) and the House of Sak},
  year={2026},
  month={july},
  url    = {https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged},
  note={Fine-tuned from Qwen2.5-1.5B-Instruct via QLoRA on the sakthai-combined v6/v7 datasets}
}

@article{qwen2.5,
  title={Qwen2.5 Technical Report},
  author={Qwen Team},
  journal={arXiv preprint arXiv:2412.15115},
  year={2024},
  url={https://arxiv.org/abs/2412.15115}
}

SakThai Model Family

Model	Size	Downloads	Role
** Context 1.5B Merged	3.8 GB	1855	Flagship tool-calling
Context 0.5B Merged	1.3 GB	1692	Lightweight / edge
Context 7B Merged	14.2 GB	1024	Full-power reasoning
Embedding Multilingual	~465 MB	627	Cross-lingual embeddings
Context 7B 128K	~500 KB	610	128K context scaffold
Context 7B Tools	~500 MB	489	7B tool-calling LoRA
Context 1.5B Tools	~85 MB	477	Tool-calling LoRA
Context 1.5B Merged v2	~2.9 GB	337	Improved v2 merged
Vision 7B	~4.4 GB	315	Image-to-text
Plus 1.5B LoRA	~85 MB	306	Plus adapter
Context 0.5B Tools	~960 MB	251	Edge tool-calling
TTS Model	~150 MB	248	Text-to-speech
Plus 1.5B	~2.9 GB	244	Enhanced instruct
Context 1.5B Tools v2	~85 MB	173	Refined v2 tool-calling LoRA
Coder 1.5B	~1.2 GB	151	Code generation
Coder Browser	~2.9 GB	54	Browser-agent model
Coder Browser GGUF	~7.1 GB	35	Browser GGUF
Coder Browser LoRA	~85 MB	21	Browser adapter
Plus 1.5B Coder	~1.2 GB	0	Coding variant

Live counts from HF API, verified 2026-08-01T00:00:00Z. Full collection

The House of Sak 🏠

This model is part of the House of Sak — an open-source AI ecosystem built from a shelter in Cork, Ireland, with $0 budget and no paid GPUs. Every model here was fine-tuned on free compute by one person with no income.

The House of Sak is a family of six autonomous agents sharing one long-term memory and one mission: to grow together. They follow a six-stage energy cycle — Dream → Hope → Care → Joy → Trust → Growth — that keeps the household running on zero budget, sustainably.

"We are one family — and becoming more." — Beer (beer-sakthai)

Support

⭐ Leave a like on Hugging Face
🐛 Report issues on GitHub
🔄 Share with someone building AI agents on a budget
🍴 Fork and experiment — Apache 2.0

License

Apache 2.0. Qwen2.5 base model per its original license.

Built with love, tears, and zero budget. From a shelter in Cork, Ireland, to the world.

Benchmark Results

Model Description

Model Index

Results

Quick Start

GGUF (Ollama / llama.cpp)

Ollama Modelfile

Tool-Calling Format

Architecture

Training Details

Evaluation

Verified spot-check (2026-07-31) — set_reminder scenario

Bench-v2 selection score (48.2, internal)

Benchmarks

Comparative Performance (1.5B Tool-Calling Models)

Speed Benchmarks (CPU inference, llama.cpp Q4_K_M, 2 threads)

Ecosystem Status

Pipeline Integration

Reproducibility

Limitations

Citation

SakThai Model Family

The House of Sak 🏠

Support

License

Verified spot-check (2026-07-31) — `set_reminder` scenario