DeepSeek Distilled 7B
Code and analytical-reasoning model.
Endpoint name vs. served weights
The model_name is deepseek-distilled-7b; the served weights are
deepseek-ai/deepseek-coder-7b-instruct. Treat it as a strong code / structured-reasoning model
and trust the hf_repo_id in the live catalog over the label.
Overview
model_name:deepseek-distilled-7b(curated base)- Source:
deepseek-ai/deepseek-coder-7b-instruct - Scale: ~7B parameters
- Served on: GPU via vLLM (transformers fallback)
- Public endpoint:
deepseek-distilled-7b-public(task typechat, billed per request in USD)
When to use
✅ Code generation and debugging, step-by-step problem solving, and analytical / logical tasks where you want clearly-structured reasoning.
❌ For open-ended creative writing or heavy multilingual chat, prefer a general model like Mistral 7B or Qwen 2.5 7B.
Live specs
Context window, VRAM footprint, price, and readiness come from the live catalog — this page does not hardcode them:
curl "https://api.colabhive.com/api/builder/v1/endpoints?visibility=public&search=deepseek"
Quick start
from colabhive import ColabHive
client = ColabHive(api_key="hive_...", account_id="YOUR_ACCOUNT_ID")
result = client.endpoints.infer(
endpoint_id="deepseek-distilled-7b-public", # SDK resolves the name to a UUID
input_data={"messages": [
{"role": "system", "content": "Show your step-by-step reasoning."},
{"role": "user", "content":
"A train travels 120 km at 60 km/h, then 180 km at 90 km/h. "
"What is the average speed for the whole trip?"},
]},
max_tokens=400,
temperature=0.3,
)
# Sync by default; a "queued" status means poll GET /tasks/{task_id}
print(result["result"] if result.get("status") != "queued"
else client.endpoints.get_task(result["task_id"]))
Tips
- Low temperature (0.2–0.4) keeps reasoning and code accurate.
- Explicitly ask it to "show your reasoning step by step" and to verify its answer.
Next steps
Authors: José Luis Minich, Maximiliano Lucius.