Instructions to use ai-forever/Zarya-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ai-forever/Zarya-0.6B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ai-forever/Zarya-0.6B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ai-forever/Zarya-0.6B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ai-forever/Zarya-0.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ai-forever/Zarya-0.6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ai-forever/Zarya-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ai-forever/Zarya-0.6B

SGLang

How to use ai-forever/Zarya-0.6B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ai-forever/Zarya-0.6B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ai-forever/Zarya-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ai-forever/Zarya-0.6B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ai-forever/Zarya-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ai-forever/Zarya-0.6B with Docker Model Runner:
```
docker model run hf.co/ai-forever/Zarya-0.6B
```

Zarya-0.6B

Zarya is a family of hybrid language models that combine a classic auto-regressive (AR) objective with a masked-diffusion (MDM) objective in one model. The architecture can be built on top of any autoregressive model but in this repository it uses the Qwen3 backbone.

Naming explanation: Zarya (pronounced as [zɐˈrʲa] (IPA notation), literally "Dawn" in English) is a figure from Slavic folklore — a female personification of dawn who may be considered a goddess. In various traditions, she can manifest as a single being or as two or three sisters simultaneously.

This is a research prototype.

Model Details

Model Description

Zarya is a research prototype of a family of hybrid language models that jointly learn a classic auto-regressive (AR) objective and a masked-diffusion (MDM) objective within a single model.

Two generation modes are supported, both reachable through a single model.generate(...) call: masked-diffusion (MDM) sampling and slotted-level speculative parallel decoding.

Model type: Hybrid auto-regressive (AR) + masked-diffusion language model (DLLM); backbone Qwen3, wrapper Zarya
Language(s) (NLP): Russian and English
License: MIT
Preprint: https://arxiv.org/abs/2609.19868
Repository with training code: https://github.com/ai-forever/zarya

Zarya-0.6B details

Zarya-0.6B has the following features:

Variant	hidden_size	num_hidden_layers	num_attention_heads	intermediate_size
Zarya-0.6B	1024	28	16	3072

Context Length: 2048

Uses

Zarya is intended for text generation. It supports conversational fine-tuning (SFT) and classic auto-regressive pretraining.

Direct Use

Direct use is text generation (continuation of a prompt) through the model.generate(...) interface, including chat-style prompts formatted with the provided chat template. Two inference modes are available through the same generate() call. Both modes fully use the KV cache with causal attention masks.

MDM sampling (slotted_generation=false): iterative masked-diffusion denoising with the first-hitting sampler.
Slotted speculative decoding (slotted_generation=true): parallel slot generation with inter-slot diffusion-based selection and intra-slot autoregressive generation for a decoding speedup.

Out-of-Scope Use

The model is a research prototype. It should not be used for production decisions, safety-critical applications, or any use case where accuracy and reliability are essential without additional evaluation and safeguards. Inference performance and stability also depend on the chosen decoding hyperparameters (like slotted_generation, slot_size, serial_num_blocks, slot_threshold, token_threshold, and others).

Bias, Risks, and Limitations

This is a research prototype. The code relies on Hugging Face Transformers APIs; when upgrading versions, compatibility must be checked (tested on Transformers 5.12.1 and PyTorch 2.9.0). Inference performance and stability depend on the choice of config parameters.

How to Get Started with the Model

Use the code below to get started with the model. Loading the model and tokenizer requires trust_remote_code=True.

import torch
from transformers import AutoModel, AutoTokenizer

model_name = "ai-forever/Zarya-0.6B"

model = AutoModel.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

prompt = "<|im_start|>user\nHello!<|im_end|>\n<|im_start|>assistant\n"
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()

# Both modes go through model.generate(...).
# With generation_config.slotted_generation=true -> slotted speculative decoding:
out = model.generate(
    input_ids,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.7,
    slot_size=16,
    serial_num_blocks=4,
    slot_threshold=0.9,
    token_threshold=0.3,
)
# Setting generation_config.slotted_generation=false -> MDM sampling instead:
# out = model.generate(input_ids, max_new_tokens=256)

print(tokenizer.decode(out[0]))

Evaluation

LM-eval benchmarking with the lm-eval package is supported. Example run:

lm_eval run \
--tasks=gsm8k,ifeval,arc_challenge,hendrycks_math500,humaneval_instruct,humaneval,mbpp,mbpp_plus,hellaswag \
--model=hf --confirm_run_unsafe_code \
--log_samples \
--apply_chat_template \
--output_path=./reports/lm-eval_results \
--model_args=pretrained=ai-forever/Zarya-0.6B,backend=causal,dtype=bfloat16,attn_implementation=sdpa,trust_remote_code=True \
--gen_kwargs slotted_generation=true,slot_size=16,serial_num_blocks=4,slot_threshold=0.9,token_threshold=0.4

Zarya-0.6B results

Measurements below were collected with varying inference parameters and on different GPUs; performance is sensitive to both, so results may differ across configurations and hardware setups.

A100, `dtype=bfloat16`, `apply_chat_template`, `slotted_generation=true,slot_size=16,serial_num_blocks=4,slot_threshold=0.9,token_threshold=0.4`

Hardware info: gpu_driver_cuda_version 13.2; gpu_driver_version 595.71.05;

Docker info: Torch: 2.9.0+cu128; Transformers: 5.12.1; CUDNN in torch: 91002; lm-eval == 0.4.12

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
gsm8k	3	flexible-extract	5	exact_match	↑	0.2646	±	0.0122
		strict-match	5	exact_match	↑	0.2646	±	0.0122
hellaswag	1	none	0	acc	↑	0.3526	±	0.0048
		none	0	acc_norm	↑	0.4270	±	0.0049
ifeval	4	none	0	inst_level_loose_acc	↑	0.5372	±	N/A
		none	0	inst_level_strict_acc	↑	0.5108	±	N/A
		none	0	prompt_level_loose_acc	↑	0.4177	±	0.0212
		none	0	prompt_level_strict_acc	↑	0.3993	±	0.0211
mbpp	1	none	3	pass_at_1	↑	0.1500	±	0.0160
mbpp_plus	1	none	3	pass_at_1	↑	0.2249	±	0.0215

H100, `dtype=bfloat16`, `apply_chat_template`, `slotted_generation=true,slot_size=16,serial_num_blocks=4,slot_threshold=0.9,token_threshold=0.4`

Hardware info: gpu_driver_cuda_version 13.0; gpu_driver_version 580.105.08;

Docker info: Torch: 2.9.0+cu128; Transformers: 5.12.1; CUDNN in torch: 91002; lm-eval == 0.4.12

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
arc_challenge	1	none	0	acc	↑	0.3063	±	0.0135
		none	0	acc_norm	↑	0.3464	±	0.0139
gsm8k	3	flexible-extract	5	exact_match	↑	0.0379	±	0.0053
		strict-match	5	exact_match	↑	0.0243	±	0.0042
hellaswag	1	none	0	acc	↑	0.3525	±	0.0048
		none	0	acc_norm	↑	0.4266	±	0.0049
hendrycks_math500	1	none	0	exact_match	↑	0.0280	±	0.0074
humaneval	1	create_test	0	pass@1	↑	0.0000	±	0
humaneval_instruct	4	create_test	0	pass@1	↑	0.0671	±	0.0196
ifeval	4	none	0	inst_level_loose_acc	↑	0.4365	±	N/A
		none	0	inst_level_strict_acc	↑	0.4161	±	N/A
		none	0	prompt_level_loose_acc	↑	0.2884	±	0.0195
		none	0	prompt_level_strict_acc	↑	0.2662	±	0.0190
mbpp	1	none	3	pass_at_1	↑	0.0000	±	0
mbpp_plus	1	none	3	pass_at_1	↑	0.0000	±	0

H100, `dtype=bfloat16`, `apply_chat_template`, `slotted_generation=false,T=0,temperature=0.5,top_p=0.8,do_sample=true,noise_schedule=linear`

Hardware info: gpu_driver_cuda_version 13.0; gpu_driver_version 580.105.08;

Docker info: Torch: 2.9.0+cu128; Transformers: 5.12.1; CUDNN in torch: 91002; lm-eval == 0.4.12

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
arc_challenge	1	none	0	acc	↑	0.3063	±	0.0135
		none	0	acc_norm	↑	0.3464	±	0.0139
gsm8k	3	flexible-extract	5	exact_match	↑	0.0091	±	0.0026
		strict-match	5	exact_match	↑	0.0000	±	0
hellaswag	1	none	0	acc	↑	0.3525	±	0.0048
		none	0	acc_norm	↑	0.4266	±	0.0049
hendrycks_math500	1	none	0	exact_match	↑	0.0000	±	0
humaneval	1	create_test	0	pass@1	↑	0.0000	±	0
humaneval_instruct	4	create_test	0	pass@1	↑	0.0000	±	0
ifeval	4	none	0	inst_level_loose_acc	↑	0.2230	±	N/A
		none	0	inst_level_strict_acc	↑	0.1906	±	N/A
		none	0	prompt_level_loose_acc	↑	0.1257	±	0.0143
		none	0	prompt_level_strict_acc	↑	0.1035	±	0.0131
mbpp	1	none	3	pass_at_1	↑	0.0000	±	0
mbpp_plus	1	none	3	pass_at_1	↑	0.0000	±	0

H100, `dtype=bfloat16`, `slotted_generation=false,T=0,temperature=0.5,top_p=0.8,do_sample=true,noise_schedule=linear`

Hardware info: gpu_driver_cuda_version 13.0; gpu_driver_version 580.126.20;

Docker info: Torch: 2.9.0+cu128; Transformers: 5.12.1; CUDNN in torch: 91002; lm-eval == 0.4.12

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
arc_challenge	1	none	0	acc	↑	0.2850	±	0.0132
		none	0	acc_norm	↑	0.3046	±	0.0134
gsm8k	3	flexible-extract	5	exact_match	↑	0.0114	±	0.0029
		strict-match	5	exact_match	↑	0.0015	±	0.0011
hellaswag	1	none	0	acc	↑	0.3295	±	0.0047
		none	0	acc_norm	↑	0.4018	±	0.0049
hendrycks_math500	1	none	0	exact_match	↑	0.0000	±	0
humaneval	1	create_test	0	pass@1	↑	0.0000	±	0
humaneval_instruct	4	create_test	0	pass@1	↑	0.0000	±	0
ifeval	4	none	0	inst_level_loose_acc	↑	0.1942	±	N/A
		none	0	inst_level_strict_acc	↑	0.1894	±	N/A
		none	0	prompt_level_loose_acc	↑	0.0980	±	0.0128
		none	0	prompt_level_strict_acc	↑	0.0943	±	0.0126
mbpp	1	none	3	pass_at_1	↑	0.0000	±	0
mbpp_plus	1	none	3	pass_at_1	↑	0.0000	±	0

H100, `dtype=bfloat16`, `slotted_generation=true,slot_size=16,serial_num_blocks=4,slot_threshold=0.9,token_threshold=0.4`

Hardware info: gpu_driver_cuda_version 13.0; gpu_driver_version 580.105.08;

Docker info: Torch: 2.9.0+cu128; Transformers: 5.12.1; CUDNN in torch: 91002; lm-eval == 0.4.12

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
arc_challenge	1	none	0	acc	↑	0.2850	±	0.0132
		none	0	acc_norm	↑	0.3046	±	0.0134
gsm8k	3	flexible-extract	5	exact_match	↑	0.0129	±	0.0031
		strict-match	5	exact_match	↑	0.0250	±	0.0043
hellaswag	1	none	0	acc	↑	0.3295	±	0.0047
		none	0	acc_norm	↑	0.4018	±	0.0049
hendrycks_math500	1	none	0	exact_match	↑	0.0000	±	0
humaneval	1	create_test	0	pass@1	↑	0.0305	±	0.0135
humaneval_instruct	4	create_test	0	pass@1	↑	0.0122	±	0.0086
ifeval	4	none	0	inst_level_loose_acc	↑	0.3921	±	N/A
		none	0	inst_level_strict_acc	↑	0.3489	±	N/A
		none	0	prompt_level_loose_acc	↑	0.2625	±	0.0189
		none	0	prompt_level_strict_acc	↑	0.2274	±	0.0180
mbpp	1	none	3	pass_at_1	↑	0.0000	±	0
mbpp_plus	1	none	3	pass_at_1	↑	0.0000	±	0

H100, `dtype=float32`, `apply_chat_template`, `slotted_generation=true,slot_size=16,serial_num_blocks=4,slot_threshold=0.9,token_threshold=0.4`

Hardware info: gpu_driver_cuda_version 13.0; gpu_driver_version 580.126.20;

Docker info: Torch: 2.9.0+cu128; Transformers: 5.12.1; CUDNN in torch: 91002; lm-eval == 0.4.12

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
arc_challenge	1	none	0	acc	↑	0.3055	±	0.0135
		none	0	acc_norm	↑	0.3456	±	0.0139
gsm8k	3	flexible-extract	5	exact_match	↑	0.0250	±	0.0043
		strict-match	5	exact_match	↑	0.0136	±	0.0032
hellaswag	1	none	0	acc	↑	0.3525	±	0.0048
		none	0	acc_norm	↑	0.4281	±	0.0049
hendrycks_math500	1	none	0	exact_match	↑	0.0200	±	0.0063
humaneval	1	create_test	0	pass@1	↑	0.0000	±	0
humaneval_instruct	4	create_test	0	pass@1	↑	0.0671	±	0.0196
ifeval	4	none	0	inst_level_loose_acc	↑	0.3993	±	N/A
		none	0	inst_level_strict_acc	↑	0.3849	±	N/A
		none	0	prompt_level_loose_acc	↑	0.2754	±	0.0192
		none	0	prompt_level_strict_acc	↑	0.2643	±	0.0190
mbpp	1	none	3	pass_at_1	↑	0.0000	±	0
mbpp_plus	1	none	3	pass_at_1	↑	0.0000	±	0

H100, `dtype=float32`, `apply_chat_template`, `slotted_generation=false,T=0,temperature=0.5,top_p=0.8,do_sample=true,noise_schedule=linear`

Hardware info: gpu_driver_cuda_version 13.0; gpu_driver_version 580.126.20;

Docker info: Torch: 2.9.0+cu128; Transformers: 5.12.1; CUDNN in torch: 91002; lm-eval == 0.4.12

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
arc_challenge	1	none	0	acc	↑	0.3055	±	0.0135
		none	0	acc_norm	↑	0.3456	±	0.0139
gsm8k	3	flexible-extract	5	exact_match	↑	0.0121	±	0.0030
		strict-match	5	exact_match	↑	0.0008	±	0.0008
hellaswag	1	none	0	acc	↑	0.3525	±	0.0048
		none	0	acc_norm	↑	0.4281	±	0.0049
hendrycks_math500	1	none	0	exact_match	↑	0.0000	±	0
humaneval	1	create_test	0	pass@1	↑	0.0000	±	0
humaneval_instruct	4	create_test	0	pass@1	↑	0.0000	±	0
ifeval	4	none	0	inst_level_loose_acc	↑	0.2362	±	N/A
		none	0	inst_level_strict_acc	↑	0.1966	±	N/A
		none	0	prompt_level_loose_acc	↑	0.1423	±	0.0150
		none	0	prompt_level_strict_acc	↑	0.1091	±	0.0134
mbpp	1	none	3	pass_at_1	↑	0.0000	±	0
mbpp_plus	1	none	3	pass_at_1	↑	0.0000	±	0

H100, `dtype=float16`, `apply_chat_template`, `slotted_generation=true,slot_size=16,serial_num_blocks=4,slot_threshold=0.9,token_threshold=0.4`

Hardware info: gpu_driver_cuda_version 13.0; gpu_driver_version 580.126.20;

Docker info: Torch: 2.9.0+cu128; Transformers: 5.12.1; CUDNN in torch: 91002; lm-eval == 0.4.12

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
arc_challenge	1	none	0	acc	↑	0.3055	±	0.0135
		none	0	acc_norm	↑	0.3447	±	0.0139
gsm8k	3	flexible-extract	5	exact_match	↑	0.0235	±	0.0042
		strict-match	5	exact_match	↑	0.0159	±	0.0034
hellaswag	1	none	0	acc	↑	0.3525	±	0.0048
		none	0	acc_norm	↑	0.4277	±	0.0049
hendrycks_math500	1	none	0	exact_match	↑	0.0180	±	0.0060
humaneval	1	create_test	0	pass@1	↑	0.0000	±	0
humaneval_instruct	4	create_test	0	pass@1	↑	0.0732	±	0.0204
ifeval	4	none	0	inst_level_loose_acc	↑	0.4077	±	N/A
		none	0	inst_level_strict_acc	↑	0.3981	±	N/A
		none	0	prompt_level_loose_acc	↑	0.2754	±	0.0192
		none	0	prompt_level_strict_acc	↑	0.2606	±	0.0189
mbpp	1	none	3	pass_at_1	↑	0.0000	±	0
mbpp_plus	1	none	3	pass_at_1	↑	0.0000	±	0

H100, `dtype=float16`, `apply_chat_template`, `slotted_generation=false,T=0,temperature=0.5,top_p=0.8,do_sample=true,noise_schedule=linear`

Hardware info: gpu_driver_cuda_version 13.0; gpu_driver_version 580.126.20;

Docker info: Torch: 2.9.0+cu128; Transformers: 5.12.1; CUDNN in torch: 91002; lm-eval == 0.4.12

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
arc_challenge	1	none	0	acc	↑	0.3055	±	0.0135
		none	0	acc_norm	↑	0.3447	±	0.0139
gsm8k	3	flexible-extract	5	exact_match	↑	0.0114	±	0.0029
		strict-match	5	exact_match	↑	0.0000	±	0
hellaswag	1	none	0	acc	↑	0.3525	±	0.0048
		none	0	acc_norm	↑	0.4277	±	0.0049
hendrycks_math500	1	none	0	exact_match	↑	0.0000	±	0
humaneval	1	create_test	0	pass@1	↑	0.0000	±	0
humaneval_instruct	4	create_test	0	pass@1	↑	0.0000	±	0
ifeval	4	none	0	inst_level_loose_acc	↑	0.2410	±	N/A
		none	0	inst_level_strict_acc	↑	0.2014	±	N/A
		none	0	prompt_level_loose_acc	↑	0.1460	±	0.0152
		none	0	prompt_level_strict_acc	↑	0.1128	±	0.0136
mbpp	1	none	3	pass_at_1	↑	0.0000	±	0
mbpp_plus	1	none	3	pass_at_1	↑	0.0000	±	0

H100, `dtype=bfloat16`, `apply_chat_template`, `slotted_generation=true,slot_size=16,serial_num_blocks=4,slot_threshold=0.9,token_threshold=0.4,max_gen_toks=2048`

Hardware info: gpu_driver_cuda_version 13.0; gpu_driver_version 580.105.08;

Docker info: Torch: 2.9.0+cu128; Transformers: 5.12.1; CUDNN in torch: 91002; lm-eval == 0.4.12

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
arc_challenge	1	none	0	acc	↑	0.3063	±	0.0135
		none	0	acc_norm	↑	0.3464	±	0.0139
gsm8k	3	flexible-extract	5	exact_match	↑	0.0182	±	0.0037
		strict-match	5	exact_match	↑	0.0023	±	0.0013
hellaswag	1	none	0	acc	↑	0.3525	±	0.0048
		none	0	acc_norm	↑	0.4266	±	0.0049
hendrycks_math500	1	none	0	exact_match	↑	0.0240	±	0.0069
humaneval	1	create_test	0	pass@1	↑	0.0000	±	0
humaneval_instruct	4	create_test	0	pass@1	↑	0.0671	±	0.0196
ifeval	4	none	0	inst_level_loose_acc	↑	0.3933	±	N/A
		none	0	inst_level_strict_acc	↑	0.3789	±	N/A
		none	0	prompt_level_loose_acc	↑	0.2680	±	0.0191
		none	0	prompt_level_strict_acc	↑	0.2606	±	0.0189
mbpp	1	none	3	pass_at_1	↑	0.0020	±	0.0020
mbpp_plus	1	none	3	pass_at_1	↑	0.0000	±	0

Citation

If you find our work helpful, please consider citing (citation will be updated after peer-reviewed publication):

@misc{sinev-etal-2026-Zarya,
  author        = {Sinev, Leonid and Koziev, Ilya and Leshchuk, Vladislav},
  title         = {Zarya: A Hybrid Autoregressive--Masked Diffusion Language Model with Flexible Training and Dual-Mode Inference},
  year          = {2026},
  archiveprefix = {arXiv},
  eprint        = {2609.19868},
  primaryclass  = {cs.CL},
  url           = {https://arxiv.org/abs/2609.19868},
}

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BF16

Collection including ai-forever/Zarya-0.6B

Zarya

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A collection of Zarya text diffusion models • 3 items • Updated 1 day ago • 1

Paper for ai-forever/Zarya-0.6B

Zarya: A Hybrid Autoregressive--Masked Diffusion Language Model with Flexible Training and Dual-Mode Inference

Paper • 2609.19868 • Published 3 days ago

Zarya-0.6B

Model Details

Model Description

Zarya-0.6B details

Uses

Direct Use

Out-of-Scope Use

Bias, Risks, and Limitations

How to Get Started with the Model

Evaluation

Zarya-0.6B results

A100, dtype=bfloat16, apply_chat_template, slotted_generation=true,slot_size=16,serial_num_blocks=4,slot_threshold=0.9,token_threshold=0.4

H100, dtype=bfloat16, apply_chat_template, slotted_generation=true,slot_size=16,serial_num_blocks=4,slot_threshold=0.9,token_threshold=0.4

H100, dtype=bfloat16, apply_chat_template, slotted_generation=false,T=0,temperature=0.5,top_p=0.8,do_sample=true,noise_schedule=linear

H100, dtype=bfloat16, slotted_generation=false,T=0,temperature=0.5,top_p=0.8,do_sample=true,noise_schedule=linear

H100, dtype=bfloat16, slotted_generation=true,slot_size=16,serial_num_blocks=4,slot_threshold=0.9,token_threshold=0.4

H100, dtype=float32, apply_chat_template, slotted_generation=true,slot_size=16,serial_num_blocks=4,slot_threshold=0.9,token_threshold=0.4

H100, dtype=float32, apply_chat_template, slotted_generation=false,T=0,temperature=0.5,top_p=0.8,do_sample=true,noise_schedule=linear

H100, dtype=float16, apply_chat_template, slotted_generation=true,slot_size=16,serial_num_blocks=4,slot_threshold=0.9,token_threshold=0.4

H100, dtype=float16, apply_chat_template, slotted_generation=false,T=0,temperature=0.5,top_p=0.8,do_sample=true,noise_schedule=linear

H100, dtype=bfloat16, apply_chat_template, slotted_generation=true,slot_size=16,serial_num_blocks=4,slot_threshold=0.9,token_threshold=0.4,max_gen_toks=2048

Citation

Collection including ai-forever/Zarya-0.6B

Paper for ai-forever/Zarya-0.6B

A100, `dtype=bfloat16`, `apply_chat_template`, `slotted_generation=true,slot_size=16,serial_num_blocks=4,slot_threshold=0.9,token_threshold=0.4`

H100, `dtype=bfloat16`, `apply_chat_template`, `slotted_generation=true,slot_size=16,serial_num_blocks=4,slot_threshold=0.9,token_threshold=0.4`

H100, `dtype=bfloat16`, `apply_chat_template`, `slotted_generation=false,T=0,temperature=0.5,top_p=0.8,do_sample=true,noise_schedule=linear`

H100, `dtype=bfloat16`, `slotted_generation=false,T=0,temperature=0.5,top_p=0.8,do_sample=true,noise_schedule=linear`

H100, `dtype=bfloat16`, `slotted_generation=true,slot_size=16,serial_num_blocks=4,slot_threshold=0.9,token_threshold=0.4`

H100, `dtype=float32`, `apply_chat_template`, `slotted_generation=true,slot_size=16,serial_num_blocks=4,slot_threshold=0.9,token_threshold=0.4`

H100, `dtype=float32`, `apply_chat_template`, `slotted_generation=false,T=0,temperature=0.5,top_p=0.8,do_sample=true,noise_schedule=linear`

H100, `dtype=float16`, `apply_chat_template`, `slotted_generation=true,slot_size=16,serial_num_blocks=4,slot_threshold=0.9,token_threshold=0.4`

H100, `dtype=float16`, `apply_chat_template`, `slotted_generation=false,T=0,temperature=0.5,top_p=0.8,do_sample=true,noise_schedule=linear`

H100, `dtype=bfloat16`, `apply_chat_template`, `slotted_generation=true,slot_size=16,serial_num_blocks=4,slot_threshold=0.9,token_threshold=0.4,max_gen_toks=2048`