What are the hardware requirements for ReflectionCoder CL 34B?

ReflectionCoder CL 34B requires approximately 67.6 GB of VRAM and supports a context window of 16K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed ReflectionCoder CL 34B and how large is it?

ReflectionCoder CL 34B is developed by SenseLLM, a model with 34b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate ReflectionCoder CL 34B?

ReflectionCoder CL 34B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

ReflectionCoder CL 34B by SenseLLM — VRAM 67.6GB, 16K context

Name: ReflectionCoder CL 34B
Author: SenseLLM

ReflectionCoder CL 34B is an open-source language model by SenseLLM. Features: 34b LLM, VRAM: 67.6GB, Context: 16K, License: apache-2.0, Code Generating, LLM Explorer Score: 0.19.

Arxiv:2405.17057 Codegen Conversational Dataset:sensellm/reflectionseq... Dataset:sensellm/reflectionseq... En Endpoints compatible Llama Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/SenseLLM/ReflectionCoder-CL-34B

ReflectionCoder CL 34B Parameters and Internals

Model Type

code generation

Use Cases

Areas:

research

Applications:

code optimization, automatic code generation

Additional Notes

ReflectionCoder effectively leverages reflection sequences constructed by integrating compiler feedback to improve code generation.

Supported Languages

en (high)

Input Output

Input Format:

<|user|>...<|assistant|>...<|endofmessage|>

Accepted Modalities:

text

Output Format:

<|text|>...<|endofblock|>

LLM Name	ReflectionCoder CL 34B
Repository 🤗	https://huggingface.co/SenseLLM/ReflectionCoder-CL-34B
Model Size	34b
Required VRAM	67.6 GB
Updated	2026-08-08
Maintainer	SenseLLM
Model Type	llama
Model Files	5.0 GB: 1-of-14 4.9 GB: 2-of-14 4.8 GB: 3-of-14 4.9 GB: 4-of-14 4.8 GB: 5-of-14 4.9 GB: 6-of-14 4.8 GB: 7-of-14 4.9 GB: 8-of-14 4.8 GB: 9-of-14 4.9 GB: 10-of-14 4.8 GB: 11-of-14 4.9 GB: 12-of-14 4.8 GB: 13-of-14 4.4 GB: 14-of-14
Supported Languages	en
Generates Code	Yes
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	16384
Model Max Length	16384
Transformers Version	4.40.1
Tokenizer Class	CodeLlamaTokenizer
Padding Token	</s>
Vocabulary Size	32011
Torch Data Type	bfloat16

Rank the ReflectionCoder CL 34B capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to ReflectionCoder CL 34B

Best Alternatives	Context / RAM	Downloads	Likes
...gpt 32K Codellama 34B Instruct	32K / 67.5 GB	56	2
CodeLlama 34B Instruct Hf	16K / 67.5 GB	22872	304
CodeLlama 34B Instruct Hf	16K / 1.4 GB	7	3
CodeLlama 34B Instruct Hf	16K / 1.4 GB	5	3
Speechless Codellama 34B V2.0	16K / 67.5 GB	96	17
CodeLlama 34B Hf	16K / 67.5 GB	27673	178
Phind CodeLlama 34B V2	16K / 67.5 GB	618	834
Phind CodeLlama 34B V1	16K / 67.5 GB	165	320
Phind CodeLlama 34B Python V1	16K / 67.5 GB	180	252
CodeLlama 34B Python Hf	16K / 67.5 GB	459	99

Note: green Score (e.g. "73.2") means that the model is better than SenseLLM/ReflectionCoder-CL-34B.