text generation models

234 models · ranked by HuggingFace downloads

Qwen3-0.6B

Qwen3-0.6B is the 0.6-billion-parameter instruction-tuned model from Alibaba Cloud's Qwen3 series, fine-tuned from the Qwen3-0.6B-Base for conversational and task-following use. It targets deployment in environments where even a 1B model is too large — edge hardware, mobile devices, or ultra-low-latency services. Apache 2.0 licensed.

19,085,165 ↓ · 1,224 ♡

gpt2

OpenAI's original GPT-2 at 124M parameters, an autoregressive language model trained on WebText (over 8 million web documents filtered from Reddit outlinks). It generates English text continuation given a prompt using next-token prediction, trained without any instruction tuning or RLHF. MIT licensed and runnable on commodity CPU hardware.

15,630,303 ↓ · 3,227 ♡

Qwen2.5-7B-Instruct

Qwen2.5-7B-Instruct is Alibaba Cloud's 7-billion-parameter instruction-tuned language model from the Qwen2.5 series, supporting English and a range of other languages. It targets applications requiring more reasoning and knowledge than sub-3B models, while remaining deployable on a single consumer GPU. Apache 2.0 licensed with text-generation-inference compatibility.

14,091,460 ↓ · 1,261 ♡

DeepSeek-V3.2

DeepSeek-V3.2 is a Mixture-of-Experts (MoE) large language model from DeepSeek AI, fine-tuned from DeepSeek-V3.2-Exp-Base. It activates a subset of expert parameters per token rather than the full model, enabling high effective parameter counts at lower per-token compute cost. MIT licensed, making it freely deployable commercially despite its scale.

11,603,059 ↓ · 1,427 ♡

Qwen3-4B-Instruct-2507

Qwen3-4B-Instruct-2507 is a 4-billion-parameter instruction-tuned model from Alibaba Cloud's Qwen3 series, updated in July 2025. It targets the mid-range deployment tier between ultra-compact sub-2B models and the 7-8B tier requiring heavier hardware. Apache 2.0 licensed with text-generation-inference compatibility.

10,823,759 ↓ · 832 ♡

Qwen3-8B

Qwen3-8B is the 8-billion-parameter instruction-tuned model from Alibaba Cloud's Qwen3 family, positioned at the competitive midpoint between 4B and 14B+ tiers. It targets deployment on single consumer or workstation GPUs while providing strong reasoning and multilingual capabilities. Apache 2.0 licensed with text-generation-inference compatibility.

10,544,379 ↓ · 1,072 ♡

Qwen2.5-1.5B-Instruct

Qwen2.5-1.5B-Instruct is a 1.5-billion-parameter instruction-tuned model from Alibaba Cloud's Qwen2.5 series, targeting edge and embedded deployment scenarios where even a 3B model is too large. Apache 2.0 licensed, it focuses on basic instruction following and short-context tasks at minimal compute cost.

9,814,007 ↓ · 686 ♡

Llama-3.1-8B-Instruct

Llama 3.1-8B-Instruct is Meta's 8-billion-parameter instruction-tuned model, supporting 8 languages including English, German, French, Spanish, Italian, Portuguese, Hindi, and Thai. Released under the Llama 3.1 license (permissive with restrictions for products over 700M users), it was a leading open-weight model at its scale at release. Context window extends to 128K tokens.

9,611,007 ↓ · 5,792 ♡

Qwen2.5-3B-Instruct

Qwen2.5-3B-Instruct is a 3-billion-parameter instruction-tuned language model from Alibaba Cloud's Qwen2.5 series, positioned between the 1.5B and 7B tiers. It targets lightweight server deployments and on-device inference scenarios where 7B is too large. The license is 'other' — requires reviewing the specific Qwen 2.5 license terms before commercial deployment.

9,441,068 ↓ · 450 ♡

opt-125m

OPT-125M is the smallest model in Meta's Open Pretrained Transformer series, a 125-million-parameter decoder-only LLM trained on a dataset comparable to GPT-3's training mix. Released as part of Meta's effort to make large language model weights accessible for research. At 125M parameters it is primarily used for prototyping, educational purposes, and compute-constrained environments.

7,996,000 ↓ · 249 ♡

tiny-Qwen2ForCausalLM-2.5

A minimal Qwen2-architecture causal LM created by the TRL (Transformer Reinforcement Learning) team for internal testing purposes. It is not intended for any production use or meaningful text generation — it exists to provide a tiny, fast-loading model compatible with Qwen2 tokenization for unit testing TRL training scripts.

7,359,406 ↓ · 6 ♡

gpt-oss-20b

GPT-OSS-20B is a 20-billion-parameter open-source language model released by OpenAI under Apache 2.0 — notable as OpenAI's first substantial open-weight release after years of closed-weights policy. Based on the gpt_oss architecture, it targets high-quality text generation at a scale deployable on research and enterprise GPU infrastructure. FP8 and MXfloat4 quantized variants reduce memory requirements.

7,070,698 ↓ · 4,587 ♡

Llama-3.2-1B-Instruct

Llama 3.2-1B-Instruct is Meta's 1-billion-parameter instruction-tuned model from the Llama 3.2 family, the smallest Llama release targeting ultra-low-resource inference scenarios. It is designed for edge deployment on devices that cannot accommodate even 3B models. The Llama 3.2 license restricts use by products/services with over 700M monthly users.

6,336,819 ↓ · 1,394 ♡

Qwen2.5-0.5B-Instruct

Qwen2.5-0.5B-Instruct is Alibaba Cloud's 0.5-billion-parameter instruction-tuned model, the smallest in the Qwen2.5 family. It targets the most resource-constrained deployment scenarios, prioritizing the ability to run on any hardware over output quality. Apache 2.0 licensed and English-focused.

6,100,069 ↓ · 507 ♡

Qwen3-32B

Qwen3-32B is Alibaba Cloud's 32-billion-parameter instruction-tuned model from the Qwen3 series, targeting deployments requiring stronger reasoning, coding, and instruction following than 7-8B models while remaining lighter than 70B+ alternatives. Apache 2.0 licensed with text-generation-inference compatibility for production serving.

4,892,134 ↓ · 692 ♡

dolphin-2.9.1-yi-1.5-34b

Dolphin 2.9.1 is a community fine-tune of Yi-1.5-34B intended to remove safety filtering and produce an 'uncensored' instruction-tuned model that follows all user requests without refusal. Trained by cognitive computations on OpenHermes, DolphinCoder, and similar datasets. Not Apache/MIT licensed — Yi-1.5-34B's base license applies.

4,709,461 ↓ · 62 ♡

Qwen3-1.7B

Qwen3-1.7B is a 1.7-billion-parameter instruction-tuned language model from Alibaba Cloud's Qwen3 series, filling the gap between the 0.6B and 4B tiers. It targets constrained deployment scenarios where sub-1B quality is insufficient but 4B VRAM requirements are too high. Apache 2.0 licensed.

4,390,063 ↓ · 457 ♡

gpt-oss-120b

OpenAI's 120B parameter open-weight language model released under Apache 2.0 in 2025. Supports MXFP4 and 8-bit quantization for multi-GPU deployment via vLLM. Competitive on reasoning and instruction-following benchmarks within the open-weight tier.

4,292,306 ↓ · 4,756 ♡

Qwen2-1.5B-Instruct

Qwen2-1.5B-Instruct is Alibaba's 1.5B parameter instruction-tuned chat model from the Qwen2 series. Designed to run efficiently on CPU or low-VRAM hardware, it handles short-context instruction-following, summarization, and Q&A tasks in English. It is the practical choice when memory constraints prevent running larger Qwen2 variants.

4,120,986 ↓ · 162 ♡

DeepSeek-R1

DeepSeek-R1 is a 671B parameter mixture-of-experts reasoning model from DeepSeek AI, trained with reinforcement learning to produce explicit chain-of-thought reasoning before answering. It achieves GPT-4-class performance on math, coding, and logical inference benchmarks and is released under an MIT license. Active parameters per forward pass are a subset of the 671B total, reducing compute per generated token.

3,819,253 ↓ · 13,319 ♡

Qwen3-4B

Qwen3-4B is Alibaba's 4B parameter model from the Qwen3 series, which introduced a hybrid thinking mode allowing the model to switch between fast direct answering and extended chain-of-thought reasoning. It is a compact model capable of running on consumer hardware while outperforming many 7B predecessors on reasoning benchmarks. Apache 2.0 licensed.

3,639,417 ↓ · 606 ♡