HuggingChat - Models - AI Chat Interfaces Tool

Overview

HuggingChat - Models is the Hugging Face curated listing and selection surface for chat-optimized language models used by the HuggingChat interface. The page aggregates community and official model repositories that are prepared or tagged for conversational use, surfacing model cards, licensing, architecture, and inference metadata so users can compare and pick models for interactive chat, research, or deployment. According to the HuggingChat models page (https://huggingface.co/chat/models), the listing emphasizes chat-ready checkpoints and shows quick indicators — for example model type, license, and whether hosted inference is available. The Models view is intended both for end users who want to switch which model backs their chat session and for developers who want to discover and evaluate conversational models before integrating them via the Hugging Face Inference API or self-hosting. Typical workflows include browsing model cards with example prompts, filtering by tags (architecture, license, task), and copying the model repository ID to call from code or a deployment pipeline. Because HuggingChat surfaces community models alongside official ones, response quality and behavior vary by model; the page links directly to model cards and documentation so users can inspect benchmarks, training data statements, and usage notes prior to selection.

Key Features

• Curated list of chat-optimized models with links to model cards and example prompts
• Quick model metadata: architecture, size, license, and hosted inference availability
• Ability to copy repo IDs to call models via Hugging Face Inference API or self-host
• Filters and search to discover models by tag, task, or popularity
• Direct links to model documentation, training notes, and community discussion

Example Usage

Example (python):

import os
import requests
from huggingface_hub import InferenceApi

# Optionally set HF_TOKEN in your environment for higher rate limits or private models
HF_TOKEN = os.getenv("HF_TOKEN")
HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}

# 1) Query the Hugging Face models API for chat-tagged results (simple search)
resp = requests.get(
    "https://huggingface.co/api/models",
    params={"search": "chat", "sort": "downloads"},
    headers=HEADERS,
)
resp.raise_for_status()
models = resp.json()
print("Top chat-related model IDs:", [m["id"] for m in models[:8]])

# 2) Pick a model ID from the list and run a simple inference using the Inference API
if models:
    model_id = models[0]["id"]
    print("Using model:", model_id)

    inference = InferenceApi(repo_id=model_id, token=HF_TOKEN)
    # Many chat models accept plain text and produce assistant responses; adjust inputs for model specifics
    output = inference("Hello! Please summarize the recent Python release notes in two sentences.")
    print("Model response:\n", output)
else:
    print("No models found by the search query.")

# Note: For production chat flows you may need a model-specific payload (conversational format),
# streaming client, or to self-host the model for low-latency/high-throughput use.

Key Information

• Category: Chat Interfaces
• Type: AI Chat Interfaces Tool