Text Generation
Transformers
Safetensors
English
qwen2
agent
conversational
ollama
small-language-model
slm
tool-use
qwen
qwen2.5
sakthai
house-of-sak
tool-calling
function-calling
merged
edge
lightweight
low-resource
raspberry-pi
on-device
benchmark
eval
Eval Results (legacy)
Eval Results
text-generation-inference
Instructions to use Nanthasit/sakthai-context-0.5b-tools with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nanthasit/sakthai-context-0.5b-tools with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Nanthasit/sakthai-context-0.5b-tools") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Nanthasit/sakthai-context-0.5b-tools") model = AutoModelForCausalLM.from_pretrained("Nanthasit/sakthai-context-0.5b-tools", 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Nanthasit/sakthai-context-0.5b-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-0.5b-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-0.5b-tools", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Nanthasit/sakthai-context-0.5b-tools
- SGLang
How to use Nanthasit/sakthai-context-0.5b-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-0.5b-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-0.5b-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-0.5b-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-0.5b-tools", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Nanthasit/sakthai-context-0.5b-tools with Docker Model Runner:
docker model run hf.co/Nanthasit/sakthai-context-0.5b-tools
Commit History
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docs: improve README with Model Description, expanded usage, Citation, and cleaned architecture table
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Fix architecture table completeness
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Improve model card: badges, architecture table, usage examples, verified model-index
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fix(model card): remove duplicated subtitle after title correction
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fix(model card): align README title with repo id and clean H1
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2026-08-01: Added Quick Example section with 30-second working tool-calling snippet
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docs: improve model card usage, format, inference snapshot
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improve model card: clean badges, add hf-hub usage, fix model-index selection value, note eval artifacts
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Upload README.md with huggingface_hub
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fix malformed README link trailing },
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fix: restore closing brace in README software citation
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fix: remove malformed URL in software citation
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Update family count: 20 public models -> 22 public models
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eval results on bench-v2 (scorer multiset-selection-v2)
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Audit fix: family collection count 20 public models / 15 datasets / 4 Spaces (39 items, bench-v3 joined)
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Update collection footer: 13 datasets -> 14 (sakthai-combined-v11 joined the family collection)
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docs: reconcile with concurrent commit 1974aad (Model Description/Limitations/Citation) — fix stale 91.8% -> 91.2% and ~990 MB -> ~988 MB in its Model Description so headline matches the multiset-corrected scoring note
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docs: add Model Description, Limitations, and Citation sections to README
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docs(round-2): correct bench-v2 selection to multiset-scored 91.2%, fix family-table sizes (0.5b-tools is merged 988 MB not LoRA; embedding 471 MB; tools-v2 73.9 MB LoRA; 7b-128k config-only; plus-1.5b-coder planned), refresh rising-star dataset counts (irrelevance 78, v7 101, bench-v2 92, food-penguin 89), add in-repo inference-check section, update sibling comparisons to corrected 48.2%/57.0%
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docs: refresh collection/family counts to live state (19 public models, 13 datasets, 4 Spaces)
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Fix broken GGUF cross-link: sakthai-context-0.5b-merged-q4_k_m.gguf → sakthai-0.5b-Q4_K_M.gguf (actual file in merged repo)
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Upload README.md with huggingface_hub
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Fix cross-link: replace dead sakthai-vision-demo Space (404) with sakthai-web-agent
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Add cross-links to v2 models (1.5b-tools-v2, 1.5b-merged-v2), bump family count 12->14
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Fix model count: 11→12 (v2 added)
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Add download badge, Spaces links, and Rising Stars promotion section
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fix: remove broken v7 model link from family table
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Upload README.md with huggingface_hub
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fix: model count 12→11 (actual from HF API)
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chore: expand discoverability tags (agent, ollama, slm, tool-use, conversational)
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fix: update model family count from 13 to 12
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fix: correct model count 14→13, remove deprecated English embedding reference
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chore: update family table — split tool rows, add v7 and exp models, emoji roles
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docs: align card to lean standard — live downloads badge, single canonical family table, drop funnel sections and stale counts
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docs: align card to lean standard — live downloads badge, single canonical family table, drop funnel sections and stale counts
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comprehensive model card: full per-category benchmark, baseline comparison, training details, methodology
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clean card: trim bloat, keep benchmark + usage + links
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cron: add cross-links for new experimental model + datasets (07-29)
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Upload README.md with huggingface_hub
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Promote v4: update model card with benchmark results and new identity
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Update ecosystem counts: 11 models, 8 datasets, 3 Spaces + add new dataset cross-links
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fix: replace broken shields.io/endpoint badge with working static badge (7 dl)
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Add sakthai-embedding to family table and low-download gems
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Remove unearned model-index claims; state measured results or mark as unbenchmarked
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docs: enrich model card - add model-index, download counts to family table, low-download gems section
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improve: add benchmark data, comparison table, and discoverability tags — promote low-download model (7 dl)
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Lean rewrite: dynamic download badge, single family table, honest evals, story→profile
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Fix card: remove dead paper link, drop unverified model-index claims, dedupe tables, fix stray markdown
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Add zero-download alert for irrelevance-supplement dataset
Promotes the sakthai-irrelevance-supplement dataset (0 downloads) from the 0.5b-tools model card. This dataset is essential for training models to decline out-of-scope tool calls but has zero downloads, making it the most under-appreciated asset in the ecosystem.fe8f70f -
fix: remove dead link to private sakthai-embedding model
SakThai AgentThe sakthai-embedding model is private (401 for unauthenticated users). Replaced the dead link with a note explaining it was superseded by sakthai-embedding-multilingual (50+ languages, 188 downloads). Also removed it from the Low-Download Gems section since unauthenticated visitors cannot access it.a934e30