Instructions to use khtsly/d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use khtsly/d with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="khtsly/d", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("khtsly/d", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("khtsly/d", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use khtsly/d with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "khtsly/d" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "khtsly/d", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/khtsly/d
- SGLang
How to use khtsly/d 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 "khtsly/d" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "khtsly/d", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "khtsly/d" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "khtsly/d", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use khtsly/d with Docker Model Runner:
docker model run hf.co/khtsly/d
periodic checkpoint @ step 200
Browse files- README.md +15 -0
- config.json +84 -0
- metrics.jsonl +23 -0
- metrics.png +0 -0
README.md
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---
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language: en
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library_name: transformers
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tags:
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- kimi-k3
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- luau
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- experimental-checkpoint
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---
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# d
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In-training periodic checkpoint (step 200). Architecture:
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Kimi K3 mini port (`KimiLinearForCausalLM`).
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**Not a finished model** -- intermediate weights uploaded during training for safekeeping/diffing.
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config.json
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{
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"architectures": [
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"KimiLinearForCausalLM"
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],
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"model_type": "kimi_linear",
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"auto_map": {
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"AutoConfig": "configuration_kimi_k3.KimiLinearConfig",
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"AutoModelForCausalLM": "modeling_kimi_linear.KimiLinearForCausalLM"
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},
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"vocab_size": 32768,
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"hidden_size": 2560,
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"num_hidden_layers": 32,
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"num_attention_heads": 20,
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"num_key_value_heads": 20,
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"max_position_embeddings": 1048576,
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"rms_norm_eps": 1e-06,
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"tie_word_embeddings": true,
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"linear_attn_config": {
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"full_attn_layers": [
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4,
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32
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],
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"kda_layers": [
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],
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"num_heads": 20,
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"head_dim": 128,
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"short_conv_kernel_size": 4,
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"gate_lower_bound": -5.0,
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"use_full_rank_gate": true
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},
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"q_lora_rank": 1280,
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"kv_lora_rank": 640,
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"qk_nope_head_dim": 128,
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"qk_rope_head_dim": 0,
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"v_head_dim": 128,
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"mla_use_nope": true,
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"mla_use_output_gate": true,
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"num_experts": 0,
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"num_experts_per_tok": 0,
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"num_shared_experts": 0,
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"moe_intermediate_size": 0,
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"routed_expert_hidden_size": 0,
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"first_k_dense_replace": 32,
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"routed_scaling_factor": 1.0,
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"moe_renormalize": false,
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"moe_router_activation_func": "sigmoid",
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"dense_ffn_hidden": 6144,
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"intermediate_size": 6144,
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"hidden_act": "situ",
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"activation_situ_beta": 4.0,
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"activation_situ_linear_beta": 25.0,
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"attn_res_block_size": 4,
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"initializer_range": 0.02
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}
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metrics.jsonl
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{"wall": 88.274, "eval_loss": 10.890216708183289, "eval_ppl": 53648.9296875, "step": 0}
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{"wall": 411.813, "step": 10, "loss": 10.885039567947388, "lm_loss": 10.885039567947388, "mtp_loss": 0.0, "lr": 0.00013560344827586205, "grad_norm": 33.359554290771484, "tokens": 2949120, "adamw_lr": 4.172413793103448e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.169921875, "loss_smoothed": 10.88545929590861, "seq_len": 4096}
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{"wall": 616.746, "step": 20, "loss": 10.876587311426798, "lm_loss": 10.876587311426798, "mtp_loss": 0.0, "lr": 0.00014120689655172414, "grad_norm": 33.370948791503906, "tokens": 5898240, "adamw_lr": 4.344827586206897e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.169921875, "loss_smoothed": 10.87702712615331, "seq_len": 4096}
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{"wall": 821.709, "step": 30, "loss": 10.862957080205282, "lm_loss": 10.862957080205282, "mtp_loss": 0.0, "lr": 0.0001468103448275862, "grad_norm": 33.463016510009766, "tokens": 8847360, "adamw_lr": 4.5172413793103454e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.169921875, "loss_smoothed": 10.86825514237086, "seq_len": 4096}
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{"wall": 1026.873, "step": 40, "loss": 10.857197324434916, "lm_loss": 10.857197324434916, "mtp_loss": 0.0, "lr": 0.0001524137931034483, "grad_norm": 33.4448356628418, "tokens": 11796480, "adamw_lr": 4.689655172413794e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.169921875, "loss_smoothed": 10.862377226352692, "seq_len": 4096}
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{"wall": 1231.558, "step": 50, "loss": 10.852633595466614, "lm_loss": 10.852633595466614, "mtp_loss": 0.0, "lr": 0.00015801724137931035, "grad_norm": 33.3587532043457, "tokens": 14745600, "adamw_lr": 4.862068965517242e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.169921875, "loss_smoothed": 10.856609841187794, "seq_len": 4096}
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{"wall": 1436.412, "step": 60, "loss": 10.848609368006388, "lm_loss": 10.848609368006388, "mtp_loss": 0.0, "lr": 0.00016362068965517242, "grad_norm": 33.31964111328125, "tokens": 17694720, "adamw_lr": 5.03448275862069e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.169921875, "loss_smoothed": 10.849376634756725, "seq_len": 4096}
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{"wall": 1641.593, "step": 70, "loss": 10.840846498807272, "lm_loss": 10.840846498807272, "mtp_loss": 0.0, "lr": 0.00016922413793103448, "grad_norm": 33.22914505004883, "tokens": 20643840, "adamw_lr": 5.2068965517241384e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.169921875, "loss_smoothed": 10.843668512503307, "seq_len": 4096}
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{"wall": 1846.552, "step": 80, "loss": 10.835049510002136, "lm_loss": 10.835049510002136, "mtp_loss": 0.0, "lr": 0.00017482758620689654, "grad_norm": 33.283485412597656, "tokens": 23592960, "adamw_lr": 5.379310344827586e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.169921875, "loss_smoothed": 10.83760959704717, "seq_len": 4096}
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{"wall": 2051.419, "step": 90, "loss": 10.829344550768534, "lm_loss": 10.829344550768534, "mtp_loss": 0.0, "lr": 0.0001804310344827586, "grad_norm": 33.317237854003906, "tokens": 26542080, "adamw_lr": 5.551724137931035e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.169921875, "loss_smoothed": 10.830694985389709, "seq_len": 4096}
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{"wall": 2258.685, "eval_loss": 10.822476506233215, "eval_ppl": 50135.0859375, "step": 100}
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{"wall": 2258.687, "step": 100, "loss": 10.818859140078226, "lm_loss": 10.818859140078226, "mtp_loss": 0.0, "lr": 0.0001860344827586207, "grad_norm": 33.2758903503418, "tokens": 29491200, "adamw_lr": 5.7241379310344835e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.169921875, "loss_smoothed": 10.822945741812386, "seq_len": 4096}
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{"wall": 2479.188, "step": 110, "loss": 10.814252654711405, "lm_loss": 10.814252654711405, "mtp_loss": 0.0, "lr": 0.00019163793103448278, "grad_norm": 33.27556610107422, "tokens": 32440320, "adamw_lr": 5.896551724137932e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.072265625, "loss_smoothed": 10.818115150928497, "seq_len": 4096}
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{"wall": 2684.107, "step": 120, "loss": 10.803292949994406, "lm_loss": 10.803292949994406, "mtp_loss": 0.0, "lr": 0.00019724137931034482, "grad_norm": 33.32582092285156, "tokens": 35389440, "adamw_lr": 6.068965517241379e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.072265625, "loss_smoothed": 10.810300902525585, "seq_len": 4096}
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{"wall": 2889.22, "step": 130, "loss": 10.799176494280497, "lm_loss": 10.799176494280497, "mtp_loss": 0.0, "lr": 0.0002028448275862069, "grad_norm": 33.18770980834961, "tokens": 38338560, "adamw_lr": 6.241379310344829e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.072265625, "loss_smoothed": 10.804276450475056, "seq_len": 4096}
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{"wall": 3094.181, "step": 140, "loss": 10.793241739273071, "lm_loss": 10.793241739273071, "mtp_loss": 0.0, "lr": 0.00020844827586206894, "grad_norm": 33.08427810668945, "tokens": 41287680, "adamw_lr": 6.413793103448276e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.072265625, "loss_smoothed": 10.798630730311077, "seq_len": 4096}
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{"wall": 3299.102, "step": 150, "loss": 10.787952423095703, "lm_loss": 10.787952423095703, "mtp_loss": 0.0, "lr": 0.00021405172413793106, "grad_norm": 33.30586624145508, "tokens": 44236800, "adamw_lr": 6.586206896551726e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.072265625, "loss_smoothed": 10.791370856761933, "seq_len": 4096}
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| 18 |
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{"wall": 3503.945, "step": 160, "loss": 10.781545042991638, "lm_loss": 10.781545042991638, "mtp_loss": 0.0, "lr": 0.0002196551724137931, "grad_norm": 33.21318435668945, "tokens": 47185920, "adamw_lr": 6.758620689655173e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.072265625, "loss_smoothed": 10.783225440979004, "seq_len": 4096}
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{"wall": 3709.172, "step": 170, "loss": 10.767958879470825, "lm_loss": 10.767958879470825, "mtp_loss": 0.0, "lr": 0.00022525862068965515, "grad_norm": 65.31437683105469, "tokens": 50135040, "adamw_lr": 6.93103448275862e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.072265625, "loss_smoothed": 10.774782085418702, "seq_len": 4096}
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{"wall": 3913.611, "step": 180, "loss": 10.770054618517557, "lm_loss": 10.770054618517557, "mtp_loss": 0.0, "lr": 0.00023086206896551727, "grad_norm": 37.112770080566406, "tokens": 53084160, "adamw_lr": 7.10344827586207e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.072265625, "loss_smoothed": 10.767992003758748, "seq_len": 4096}
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{"wall": 4118.728, "step": 190, "loss": 10.752914865811666, "lm_loss": 10.752914865811666, "mtp_loss": 0.0, "lr": 0.0002364655172413793, "grad_norm": 33.216190338134766, "tokens": 56033280, "adamw_lr": 7.275862068965517e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.072265625, "loss_smoothed": 10.75819280942281, "seq_len": 4096}
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{"wall": 4325.819, "eval_loss": 10.744163990020752, "eval_ppl": 46358.6640625, "step": 200}
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{"wall": 4325.821, "step": 200, "loss": 10.74438444773356, "lm_loss": 10.74438444773356, "mtp_loss": 0.0, "lr": 0.0002420689655172414, "grad_norm": 33.22344207763672, "tokens": 58982400, "adamw_lr": 7.448275862068966e-05, "mem_alloc_gb": 14.685915470123291, "mem_reserv_gb": 79.072265625, "loss_smoothed": 10.748678056399028, "seq_len": 4096}
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metrics.png
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