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https://github.com/eugenehp/llama-cpp-rs
A wrapper around the llama-cpp library for rust, including new Sampler API from llama-cpp.
https://github.com/eugenehp/llama-cpp-rs
Last synced: about 1 month ago
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A wrapper around the llama-cpp library for rust, including new Sampler API from llama-cpp.
- Host: GitHub
- URL: https://github.com/eugenehp/llama-cpp-rs
- Owner: eugenehp
- Created: 2024-11-23T08:30:15.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-11-23T08:37:09.000Z (about 1 month ago)
- Last Synced: 2024-11-23T09:23:52.887Z (about 1 month ago)
- Language: Rust
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🦙 [llama-cpp-rs][readme]   [![Docs]][docs.rs] [![Latest Version]][crates.io] [![Lisence]][crates.io]
[Docs]: https://img.shields.io/docsrs/llama-cpp-4.svg
[Latest Version]: https://img.shields.io/crates/v/llama-cpp-4.svg
[crates.io]: https://crates.io/crates/llama-cpp-4
[docs.rs]: https://docs.rs/llama-cpp-4
[Lisence]: https://img.shields.io/crates/l/llama-cpp-4.svg
[llama-cpp-sys]: https://crates.io/crates/llama-cpp-sys-4
[readme]: https://github.com/eugenehp/llama-cpp-rs/tree/main/llama-cpp-4
This is the home for [llama-cpp-4][crates.io]. It also contains the [llama-cpp-sys] bindings which are updated regularly
and in sync with [llama-cpp-4][crates.io].
This project was created with the explict goal of staying as up to date as possible with llama.cpp, as a result it is
dead simple, very close to raw bindings, and does not follow semver meaningfully.
Check out the [docs.rs] for crate documentation or the [readme] for high level information about the project.
## Try it
We maintain a super simple example of using the library:
Clone the repo
```bash
git clone --recursive https://github.com/eugenehp/llama-cpp-rs
cd llama-cpp-rs
```
Run the simple example (add `--featues cuda` if you have a cuda gpu)
```bash
cargo run --release --bin simple -- --prompt "The way to kill a linux process is" hf-model TheBloke/Llama-2-7B-GGUF llama-2-7b.Q4_K_M.gguf
```
Output
ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no
ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes
ggml_init_cublas: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes
llama_model_params { n_gpu_layers: 1000, split_mode: 1, main_gpu: 0, tensor_split: 0x0, progress_callback: None, progress_callback_user_data: 0x0, kv_overrides: 0x0, vocab_only: false, use_mmap: true, use_mlock: false }
llama_model_loader: loaded meta data with 19 key-value pairs and 291 tensors from /home/marcus/.cache/huggingface/hub/models--TheBloke--Llama-2-7B-GGUF/snapshots/b4e04e128f421c93a5f1e34ac4d7ca9b0af47b80/llama-2-7b.Q4_K_M.gguf (version GGUF V2)
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.name str = LLaMA v2
llama_model_loader: - kv 2: llama.context_length u32 = 4096
llama_model_loader: - kv 3: llama.embedding_length u32 = 4096
llama_model_loader: - kv 4: llama.block_count u32 = 32
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 11008
llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 7: llama.attention.head_count u32 = 32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 32
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 10: general.file_type u32 = 15
llama_model_loader: - kv 11: tokenizer.ggml.model str = llama
llama_model_loader: - kv 12: tokenizer.ggml.tokens arr[str,32000] = ["", "", "", "<0x00>", "<...
llama_model_loader: - kv 13: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv 15: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 17: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 18: general.quantization_version u32 = 2
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type q4_K: 193 tensors
llama_model_loader: - type q6_K: 33 tensors
llm_load_vocab: special tokens definition check successful ( 259/32000 ).
llm_load_print_meta: format = GGUF V2
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32000
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 4096
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 32
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: n_embd_k_gqa = 4096
llm_load_print_meta: n_embd_v_gqa = 4096
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: n_ff = 11008
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 4096
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: model type = 7B
llm_load_print_meta: model ftype = Q4_K - Medium
llm_load_print_meta: model params = 6.74 B
llm_load_print_meta: model size = 3.80 GiB (4.84 BPW)
llm_load_print_meta: general.name = LLaMA v2
llm_load_print_meta: BOS token = 1 ''
llm_load_print_meta: EOS token = 2 ''
llm_load_print_meta: UNK token = 0 ''
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.22 MiB
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 33/33 layers to GPU
llm_load_tensors: CUDA0 buffer size = 3820.94 MiB
llm_load_tensors: CPU buffer size = 70.31 MiB
..................................................................................................
Loaded "/home/marcus/.cache/huggingface/hub/models--TheBloke--Llama-2-7B-GGUF/snapshots/b4e04e128f421c93a5f1e34ac4d7ca9b0af47b80/llama-2-7b.Q4_K_M.gguf"
llama_new_context_with_model: n_ctx = 2048
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA0 KV buffer size = 1024.00 MiB
llama_new_context_with_model: KV self size = 1024.00 MiB, K (f16): 512.00 MiB, V (f16): 512.00 MiB
llama_new_context_with_model: CUDA_Host input buffer size = 13.02 MiB
ggml_gallocr_reserve_n: reallocating CUDA0 buffer from size 0.00 MiB to 164.01 MiB
ggml_gallocr_reserve_n: reallocating CUDA_Host buffer from size 0.00 MiB to 8.00 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 164.01 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 8.00 MiB
llama_new_context_with_model: graph splits (measure): 3
n_len = 32, n_ctx = 2048, k_kv_req = 32
The way to kill a linux process is to send it a SIGKILL signal.
The way to kill a windows process is to send it a S
decoded 24 tokens in 0.23 s, speed 105.65 t/s
load time = 727.50 ms
sample time = 0.46 ms / 24 runs (0.02 ms per token, 51835.85 tokens per second)
prompt eval time = 68.52 ms / 9 tokens (7.61 ms per token, 131.35 tokens per second)
eval time = 225.70 ms / 24 runs (9.40 ms per token, 106.34 tokens per second)
total time = 954.18 ms
### Chat example
```shell
cd examples/chat
cargo run -- local ../../qwen2-1_5b-instruct-q4_0.gguf
```
## Development
Ensure that when you clone this project you also clone the submodules. This can be done with the following command:
```sh
git clone --recursive https://github.com/eugenehp/llama-cpp-rs
```
or if you have already cloned the project you can run:
```sh
git submodule update --init --recursive
```
## Thanks
Most of the work was done [here](https://crates.io/crates/llama-cpp-2), thanks to their contributors.
## Other libraries
Check out [bitnet-cpp](https://github.com/eugenehp/bitnet-cpp-rs) wrapper in Rust for highly quantized models.