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cerebras/
Llama-3-CBHybridM-8B

Name: Llama-3-CBHybridM-8B
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Llama-3-CBHybridM-8B: Model Information

We are excited to release the Cerebras hybrid dense/sparse attention versions of Llama-3.1-8B-Instruct models optimized for long-context performance. This series includes two models: Llama3.1-CBHybridL-8B (model with 25 sparse attention layers out of 32) and Llama3.1-CBHybridM-8B (28 sparse attention layers out of 32).

This model – Cerebras Llama3.1-CBHybridM-8B – was built on top of Llama-3.1-8B-Instruct using sparse attention training features available in Cerebras Model Zoo Release 2.4. We created hybrid versions of Llama-3.1-8B-Instruct with most of the self-attention layers fine-tuned to perform sparse lambda-mask attention which reduces KV cache memory usage by 1.6-1.7x while largely maintaining long-context performance.

You can find more information about Cerebras hybrid Llama models at the following locations:

Results

Our hybrid models retain most of their performance in long-context despite requiring much less memory for KV cache:

LongBench suite	Llama-3.1-8B-Instruct	Llama-3-CBHybridM-8B	Llama-3-CBHybridL-8B
KV cache memory*, GB	2.147	1.275	1.376
Single-doc QA	54.197	54.507	56.187
Multi-doc QA	41.455	41.022	43.082
Summarization	26.1275	25.607	25.357
Few-shot learning	63.4075	64.42	65.183
Synthetic	97.29	96.75	98.0
Code completion	59.745	66.865	66.49
Macro-mean (EN & ZH)	57.037	58.195	59.05
Macro-mean (EN)	58.606	60.485	60.937

HELMET suite (seq. len. 16K)	Llama-3.1-8B-Instruct	Llama-3-CBHybridM-8B	Llama-3-CBHybridL-8B
KV cache memory, GB	2.147	1.275	1.376
Recall	99.6875	87.5625	95.1875
Rerank	52.6671	42.7879	45.5175
RAG	69.0417	68.625	69.4583
LongdocQA	32.061	34.419	35.2879
ICL	76	81.6	82.2
Summarization	26.278	22.4353	23.7324
Macro-mean	59.2892	56.2382	58.564

* we include KV cache memory usage numbers at a representative sequence length of 16K, however note that samples across LongBench tasks have variable length, with ~14.5K being the 75th percentile of the sample length distribution.

Example Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "cerebras/Llama-3-CBHybridM-8B"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

messages = [
    {"role": "system", "content": "You are a wafer-scale chatbot who always responds in wafer speak!"},
    {"role": "user", "content": "Who are you?"},
]

input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    input_ids,
    max_new_tokens=256,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))

Adding memory tokens for enhanced long-context performance

We found that adding auxiliary memory tokens to input sequences at regular intervals improves long-context performance. These tokens can be inserted into the input sequence using a helper tokenizer.insert_memory_tokens() method as shown below:

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "cerebras/Llama-3-CBHybridM-8B"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

messages = [
    {"role": "system", "content": "You are a wafer-scale chatbot who always responds in wafer speak!"},
    {"role": "user", "content": "Who are you?"},
]

input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

# Inserting 8 memory tokens per 256 tokens of original input:
input_ids = tokenizer.insert_memory_tokens(
    input_ids,
    episode_length=256,
    num_memory_tokens_per_episode=8
)

outputs = model.generate(
    input_ids,
    max_new_tokens=256,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))

In our ablations, inserting 8 memory tokens after every 256 tokens of original input resulted in best accuracy. See out blog post for mode details.

License

Llama3.1 Community License

Acceptable Use Policy

Acknowledgements

Our models are fine-tuned versions of Meta-Llama-3.1-8B-Instruct. The sparse attention mechanism used in the Llama-3-CBHybrid model series is from the LM-Infinite work of Han et al. See our blog post for the full list of references.

Citing this work

bibtex

@misc{cerebras2025cb-hybrid-llama,
  author       = {Lazarevich, Ivan and Hassanpour, Mohammad and Venkatesh, Ganesh},
  title        = {Compressing KV cache memory by half with sparse attention},
  month        = {March},
  year         = {2025},
  howpublished = {\url{https://www.cerebras.ai/blog/compressing-kv-cache-memory-by-half-with-sparse-attention}}
}​​​​