tiiuae_Falcon-H1R-7B-GGUF
Falcon-H1R-7B from TII (Technology Innovation Institute) is a 7-billion-parameter reasoning-specialized causal decoder-only model built on the Falcon-H1-7B-Base foundation, featuring a hybrid Transformer + Mamba2 architecture trained via cold-start supervised fine-tuning with long reasoning traces and scaled RL using GRPO (Generalized Reward Preference Optimization) for exceptional performance in mathematics, programming, instruction following, and general logic. It achieves state-of-the-art results among <8B models across benchmarks like 88.1% on AIME24 (96.7% with test-time scaling), 68.6% on LiveCodeBench v5-v6, 61.3% on GPQA-Diamond, 72.1% on MMLU-Pro, and 53.4% on IFBench—often matching or exceeding 14B-47B competitors like Qwen3-32B, Phi-4-14B, and Nemotron-H-47B while enabling 2x faster inference (e.g., ~1800 tokens/s/GPU at batch=64) and up to 262k context length with low memory footprint. Optimized for multilingual use (English primary, trained on 18 languages including Arabic, Hindi, Chinese) under Falcon-LLM License, it generates structured ... reasoning blocks followed by final answers, deployable via Transformers (temperature=0.6, top_p=0.95, max_new_tokens=65536), vLLM (>=0.11.0, --reasoning-parser deepseek_r1), or SGLang for efficient real-world applications on TP=2 setups.
Quick Start with llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF",
filename="Falcon-H1R-7B.Q4_K_M.gguf",
)
llm.create_chat_completion(
messages = [
{
"role": "user",
"content": "What is the capital of France?"
}
]
)
Falcon-H1R-7B [GGUF]
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| Falcon-H1R-7B-bf16.gguf | BF16 | 15.2 GB | Download |
| Falcon-H1R-7B-f32.gguf | F32 | 30.3 GB | Download |
| Falcon-H1R-7B.IQ4_XS.gguf | IQ4_XS | 4.19 GB | Download |
| Falcon-H1R-7B.Q2_K.gguf | Q2_K | 2.89 GB | Download |
| Falcon-H1R-7B.Q3_K_L.gguf | Q3_K_L | 3.92 GB | Download |
| Falcon-H1R-7B.Q3_K_M.gguf | Q3_K_M | 3.69 GB | Download |
| Falcon-H1R-7B.Q3_K_S.gguf | Q3_K_S | 3.43 GB | Download |
| Falcon-H1R-7B.Q4_K_M.gguf | Q4_K_M | 4.6 GB | Download |
| Falcon-H1R-7B.Q4_K_S.gguf | Q4_K_S | 4.4 GB | Download |
| Falcon-H1R-7B.Q5_K_M.gguf | Q5_K_M | 5.39 GB | Download |
| Falcon-H1R-7B.Q5_K_S.gguf | Q5_K_S | 5.28 GB | Download |
| Falcon-H1R-7B.Q6_K.gguf | Q6_K | 6.23 GB | Download |
| Falcon-H1R-7B.Q8_0.gguf | Q8_0 | 8.07 GB | Download |
| Falcon-H1R-7B.f16.gguf | F16 | 15.2 GB | Download |
Quants Usage
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
- Downloads last month
- 389
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