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Model: Nanthasit/sakthai-context-1.5b-merged Source: Original Platform
2026-07-10 03:23:10 +08:00
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+---
+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}}
+}
+```