Text Generation
MLX
Safetensors
deepseek_v4
jang
jangtq
jangtq2
jangtq-prestack
mxtq
deepseek
deepseek-v4
deepseek-v4-flash
Mixture of Experts
mla
hash-layers
mtp
apple-silicon
osaurus
Instructions to use OsaurusAI/DeepSeek-V4-Flash-JANGTQ2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OsaurusAI/DeepSeek-V4-Flash-JANGTQ2 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("OsaurusAI/DeepSeek-V4-Flash-JANGTQ2") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- MLX LM
How to use OsaurusAI/DeepSeek-V4-Flash-JANGTQ2 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "OsaurusAI/DeepSeek-V4-Flash-JANGTQ2" --prompt "Once upon a time"
| { | |
| "architectures": [ | |
| "DeepseekV4ForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 0, | |
| "eos_token_id": [ | |
| 1, | |
| 128803 | |
| ], | |
| "expert_dtype": "fp4", | |
| "hc_eps": 1e-06, | |
| "hc_mult": 4, | |
| "hc_sinkhorn_iters": 20, | |
| "head_dim": 512, | |
| "hidden_act": "silu", | |
| "hidden_size": 4096, | |
| "index_head_dim": 128, | |
| "index_n_heads": 64, | |
| "index_topk": 512, | |
| "initializer_range": 0.02, | |
| "max_position_embeddings": 1048576, | |
| "model_type": "deepseek_v4", | |
| "moe_intermediate_size": 2048, | |
| "n_routed_experts": 256, | |
| "n_shared_experts": 1, | |
| "norm_topk_prob": true, | |
| "num_attention_heads": 64, | |
| "num_experts_per_tok": 6, | |
| "num_hidden_layers": 43, | |
| "num_hash_layers": 3, | |
| "num_key_value_heads": 1, | |
| "num_nextn_predict_layers": 1, | |
| "o_groups": 8, | |
| "o_lora_rank": 1024, | |
| "q_lora_rank": 1024, | |
| "qk_rope_head_dim": 64, | |
| "rms_norm_eps": 1e-06, | |
| "rope_theta": 10000, | |
| "routed_scaling_factor": 1.5, | |
| "scoring_func": "sqrtsoftplus", | |
| "sliding_window": 128, | |
| "swiglu_limit": 10.0, | |
| "tie_word_embeddings": false, | |
| "topk_method": "noaux_tc", | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.57.1", | |
| "use_cache": true, | |
| "vocab_size": 129280, | |
| "compress_rope_theta": 160000, | |
| "compress_ratios": [ | |
| 0, | |
| 0, | |
| 4, | |
| 128, | |
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| 0 | |
| ], | |
| "rope_parameters": { | |
| "beta_fast": 32.0, | |
| "beta_slow": 1.0, | |
| "factor": 16.0, | |
| "original_max_position_embeddings": 65536, | |
| "rope_type": "yarn", | |
| "rope_theta": 10000.0 | |
| }, | |
| "quantization": { | |
| "bits": 8, | |
| "group_size": 32, | |
| "mode": "affine", | |
| "routed_expert_bits": 2, | |
| "routed_expert_bit_plan": { | |
| "default_bits": 2, | |
| "codec": "mxtq" | |
| }, | |
| "mxtq_bits": { | |
| "routed_expert": 2, | |
| "attention": 8, | |
| "shared_expert": 8, | |
| "compressor": 8, | |
| "indexer": 8, | |
| "embed_tokens": 8, | |
| "lm_head": 8, | |
| "norms_router_hc": 16 | |
| } | |
| }, | |
| "weight_format": "mxtq", | |
| "routed_expert_bits": 2, | |
| "routed_expert_bit_plan": { | |
| "default_bits": 2, | |
| "codec": "mxtq" | |
| }, | |
| "mxtq_bits": { | |
| "routed_expert": 2, | |
| "attention": 8, | |
| "shared_expert": 8, | |
| "compressor": 8, | |
| "indexer": 8, | |
| "embed_tokens": 8, | |
| "lm_head": 8, | |
| "norms_router_hc": 16 | |
| }, | |
| "mxtq_seed": 42, | |
| "group_size": 32, | |
| "_name_or_path": "DSV4-Flash-JANGTQ2" | |
| } |