Mistral 7B Instruct
Balanced, general-purpose LLM with an excellent quality-to-size ratio.
Overview
model_name:mistral-7b-instruct(curated base)- Source:
mistralai/Mistral-7B-Instruct-v0.3 - Scale: ~7B parameters
- Served on: GPU via vLLM (transformers fallback)
- Public endpoint:
mistral-7b-instruct-public(task typechat, billed per request in USD)
When to use
✅ General-purpose chat, customer-support agents, summarization, Q&A, content generation — a solid default for most production text workloads.
❌ For code, prefer Qwen 2.5 Coder; for heavier multilingual work,
Qwen 2.5 7B; for higher quality, step up to gpt-oss-20b or
mistral-small-24b (see All LLMs).
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=mistral-7b"
Quick start
from colabhive import ColabHive
client = ColabHive(api_key="hive_...", account_id="YOUR_ACCOUNT_ID")
result = client.endpoints.infer(
endpoint_id="mistral-7b-instruct-public", # SDK resolves the name to a UUID
input_data={"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "How can I integrate your API?"},
]},
max_tokens=500,
temperature=0.7,
)
# 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"]))
The same model is also reachable through the OpenAI-compatible /v1/chat/completions surface,
addressed by model name or endpoint UUID.
Tips
- Define role and tone in a clear
systemprompt. - Temperature around 0.7 balances consistency and variety; drop to 0.3–0.5 for factual answers.
- OpenAI-compatible streaming is incremental on current node runtimes and falls back to one complete SSE chunk on older nodes — see All LLMs.
Next steps
Authors: José Luis Minich, Maximiliano Lucius.