---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- qwen2
- sakthai
- tool-calling
- instruct
- lora
datasets:
- Nanthasit/sakthai-combined-v4
base_model: Qwen/Qwen2.5-1.5B-Instruct
model-index:
- name: sakthai-context-1.5b-merged
  results:
  - task:
      type: text-generation
      name: Tool-Calling & Instruction Following
    dataset:
      name: SakThai Eval Suite
      type: Nanthasit/sakthai-combined-v4
    metrics:
    - type: pass_rate
      value: 100
      name: Overall Pass Rate (45/45)
    - type: pass_rate
      value: 100
      name: Basic (6/6)
    - type: pass_rate
      value: 100
      name: Multi-Turn (9/9)
    - type: pass_rate
      value: 100
      name: Instruction Following (6/6)
    - type: pass_rate
      value: 100
      name: Tool Calling (6/6)
    - type: pass_rate
      value: 100
      name: Reasoning (6/6)
    - type: pass_rate
      value: 100
      name: Format Adherence (12/12)
---

# SakThai Context 1.5B

Fine-tuned from **Qwen2.5-1.5B-Instruct** on the SakThai combined dataset for **tool-calling, multi-turn context, and instruction-following** capabilities. Designed as the reasoning backbone for the SakThai agent (running on Hermes Agent framework).

## Model Details

| Property | Value |
|----------|-------|
| **Base Model** | [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) |
| **Architecture** | Qwen2 (decoder-only transformer) |
| **Hidden Size** | 1536 |
| **Layers** | 28 |
| **Attention Heads** | 12 |
| **Intermediate Size** | 8960 |
| **Vocab Size** | 151936 |
| **Fine-tuning Method** | LoRA (r=16, α=32, dropout=0.1) |
| **Target Modules** | q_proj, k_proj, v_proj, o_proj |
| **Training Steps** | 220 |
| **Training Duration** | ~39 minutes (4 epochs on 974 examples) |
| **License** | Apache 2.0 |

## Training

- **Base model:** Qwen/Qwen2.5-1.5B-Instruct
- **Dataset:** [Nanthasit/sakthai-combined-v4](https://huggingface.co/datasets/Nanthasit/sakthai-combined-v4)
  — 974 training + 51 test examples covering 25 canonical tool schemas
- **Method:** LoRA via PEFT (rank=16, alpha=32, dropout=0.1) on q/k/v/o projections
- **Optimizer:** AdamW, linear schedule, 220 steps

> The LoRA adapter weights are available at [Nanthasit/sakthai-context-1.5b-tools](https://huggingface.co/Nanthasit/sakthai-context-1.5b-tools).

## Evaluation

**45/45 tests passed (100%)** across 3 runs (15 tests/run).

| Category | Tests | Pass Rate |
|----------|:-----:|:---------:|
| Basic (greeting, identity) | 6 | ✅ 100% |
| Multi-turn (name recall, context, preference) | 9 | ✅ 100% |
| Instruction following | 6 | ✅ 100% |
| Tool calling | 6 | ✅ 100% |
| Reasoning (math, coding, explanation) | 6 | ✅ 100% |
| Format adherence (JSON, markdown, arrays) | 12 | ✅ 100% |

Full eval report: [`eval/EVAL.md`](https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged/blob/main/eval/EVAL.md)

## Usage

### Via Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Nanthasit/sakthai-context-1.5b-merged")
tokenizer = AutoTokenizer.from_pretrained("Nanthasit/sakthai-context-1.5b-merged")

messages = [{"role": "user", "content": "What's the capital of Japan?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Via Inference Client

```python
from huggingface_hub import InferenceClient

client = InferenceClient("Nanthasit/sakthai-context-1.5b-merged")
response = client.chat_completion(
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=256
)
print(response.choices[0].message.content)
```

### Merging the Adapter

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base, "Nanthasit/sakthai-context-1.5b-tools")
merged = model.merge_and_unload()
merged.save_pretrained("./sakthai-context-1.5b-merged")
```

## Limitations

- Fine-tuned primarily for tool-calling and structured output; general knowledge remains at Qwen2.5-1.5B-Instruct baseline level.
- Tested on CPU — performance on GPU inference may produce slightly different output distributions.
- Best suited for agentic workflows with well-defined tool schemas. Complex multi-hop reasoning may require a larger base model.

## Bias, Risks & Safety

This model is fine-tuned from Qwen2.5-1.5B-Instruct and inherits its base strengths and limitations. As a small language model (1.5B parameters), it may exhibit:

- Factual inaccuracies on niche or recent topics
- Biases present in the base model's pre-training data
- Limited performance on tasks requiring long context (>2K tokens) or deep multi-step reasoning

Deploy with appropriate guardrails for any user-facing application.

## Citation

```bibtex
@misc{sakthai-context-1.5b,
  author = {Nanthasit},
  title = {SakThai Context 1.5B — Tool-Calling Fine-Tune},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged}}
}
```