Module: LLaMACpp
- Defined in:
- lib/llama_cpp.rb,
lib/llama_cpp/version.rb,
ext/llama_cpp/llama_cpp.cpp
Overview
llama_cpp.rb provides Ruby bindings for the llama.cpp.
Constant Summary collapse
- Params =
Class alias to match interface of whispercpp gem.
ContextParams
- VERSION =
The version of llama_cpp.rb you install.
'0.4.0'
- LLAMA_CPP_VERSION =
The version of llama.cpp bundled with llama_cpp.rb.
'b1060'
- LLAMA_MAX_DEVICES =
INT2NUM(LLAMA_MAX_DEVICES)
- LLAMA_LOG_LEVEL_ERROR =
INT2NUM(LLAMA_LOG_LEVEL_ERROR)
- LLAMA_LOG_LEVEL_WARN =
INT2NUM(LLAMA_LOG_LEVEL_WARN)
- LLAMA_LOG_LEVEL_INFO =
INT2NUM(LLAMA_LOG_LEVEL_INFO)
- LLAMA_VOCAB_TYPE_SPM =
INT2NUM(LLAMA_VOCAB_TYPE_SPM)
- LLAMA_VOCAB_TYPE_BPE =
INT2NUM(LLAMA_VOCAB_TYPE_BPE)
- LLAMA_TOKEN_TYPE_UNDEFINED =
INT2NUM(LLAMA_TOKEN_TYPE_UNDEFINED)
- LLAMA_TOKEN_TYPE_NORMAL =
INT2NUM(LLAMA_TOKEN_TYPE_NORMAL)
- LLAMA_TOKEN_TYPE_UNKNOWN =
INT2NUM(LLAMA_TOKEN_TYPE_UNKNOWN)
- LLAMA_TOKEN_TYPE_CONTROL =
INT2NUM(LLAMA_TOKEN_TYPE_CONTROL)
- LLAMA_TOKEN_TYPE_USER_DEFINED =
INT2NUM(LLAMA_TOKEN_TYPE_USER_DEFINED)
- LLAMA_TOKEN_TYPE_UNUSED =
INT2NUM(LLAMA_TOKEN_TYPE_UNUSED)
- LLAMA_TOKEN_TYPE_BYTE =
INT2NUM(LLAMA_TOKEN_TYPE_BYTE)
- LLAMA_FTYPE_ALL_F32 =
INT2NUM(LLAMA_FTYPE_ALL_F32)
- LLAMA_FTYPE_MOSTLY_F16 =
INT2NUM(LLAMA_FTYPE_MOSTLY_F16)
- LLAMA_FTYPE_MOSTLY_Q4_0 =
INT2NUM(LLAMA_FTYPE_MOSTLY_Q4_0)
- LLAMA_FTYPE_MOSTLY_Q4_1 =
INT2NUM(LLAMA_FTYPE_MOSTLY_Q4_1)
- LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 =
INT2NUM(LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16)
- LLAMA_FTYPE_MOSTLY_Q8_0 =
INT2NUM(LLAMA_FTYPE_MOSTLY_Q8_0)
- LLAMA_FTYPE_MOSTLY_Q5_0 =
INT2NUM(LLAMA_FTYPE_MOSTLY_Q5_0)
- LLAMA_FTYPE_MOSTLY_Q5_1 =
INT2NUM(LLAMA_FTYPE_MOSTLY_Q5_1)
- LLAMA_FTYPE_MOSTLY_Q2_K =
INT2NUM(LLAMA_FTYPE_MOSTLY_Q2_K)
- LLAMA_FTYPE_MOSTLY_Q3_K_S =
INT2NUM(LLAMA_FTYPE_MOSTLY_Q3_K_S)
- LLAMA_FTYPE_MOSTLY_Q3_K_M =
INT2NUM(LLAMA_FTYPE_MOSTLY_Q3_K_M)
- LLAMA_FTYPE_MOSTLY_Q3_K_L =
INT2NUM(LLAMA_FTYPE_MOSTLY_Q3_K_L)
- LLAMA_FTYPE_MOSTLY_Q4_K_S =
INT2NUM(LLAMA_FTYPE_MOSTLY_Q4_K_S)
- LLAMA_FTYPE_MOSTLY_Q4_K_M =
INT2NUM(LLAMA_FTYPE_MOSTLY_Q4_K_M)
- LLAMA_FTYPE_MOSTLY_Q5_K_S =
INT2NUM(LLAMA_FTYPE_MOSTLY_Q5_K_S)
- LLAMA_FTYPE_MOSTLY_Q5_K_M =
INT2NUM(LLAMA_FTYPE_MOSTLY_Q5_K_M)
- LLAMA_FTYPE_MOSTLY_Q6_K =
INT2NUM(LLAMA_FTYPE_MOSTLY_Q6_K)
- LLAMA_FTYPE_GUESSED =
INT2NUM(LLAMA_FTYPE_GUESSED)
- LLAMA_GRETYPE_END =
INT2NUM(LLAMA_GRETYPE_END)
- LLAMA_GRETYPE_ALT =
INT2NUM(LLAMA_GRETYPE_ALT)
- LLAMA_GRETYPE_RULE_REF =
INT2NUM(LLAMA_GRETYPE_RULE_REF)
- LLAMA_GRETYPE_CHAR =
INT2NUM(LLAMA_GRETYPE_CHAR)
- LLAMA_GRETYPE_CHAR_NOT =
INT2NUM(LLAMA_GRETYPE_CHAR_NOT)
- LLAMA_GRETYPE_CHAR_RNG_UPPER =
INT2NUM(LLAMA_GRETYPE_CHAR_RNG_UPPER)
- LLAMA_GRETYPE_CHAR_ALT =
INT2NUM(LLAMA_GRETYPE_CHAR_ALT)
- LLAMA_FILE_MAGIC_GGSN =
rb_str_new2(ss_magic.str().c_str())
- LLAMA_SESSION_MAGIC =
rb_str_new2(ss_magic.str().c_str())
- LLAMA_DEFAULT_SEED =
rb_str_new2(ss_magic.str().c_str())
- LLAMA_SESSION_VERSION =
rb_str_new2(std::to_string(LLAMA_SESSION_VERSION).c_str())
Class Method Summary collapse
- .backend_free ⇒ Object
-
.backend_init(*args) ⇒ Object
module functions.
-
.generate(context, prompt, n_predict: 128, n_threads: 1, n_keep: 10, n_batch: 512, repeat_last_n: 64, repeat_penalty: 1.1, frequency: 0.0, presence: 0.0, top_k: 40, top_p: 0.95, tfs_z: 1.0, typical_p: 1.0, temperature: 0.8) ⇒ String
Generates sentences following the given prompt for operation check.
- .max_devices ⇒ Object
- .mlock_supported? ⇒ Boolean
- .mmap_supported? ⇒ Boolean
- .model_quantize(*args) ⇒ Object
- .print_system_info ⇒ Object
- .time_us ⇒ Object
Class Method Details
.backend_free ⇒ Object
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# File 'ext/llama_cpp/llama_cpp.cpp', line 2456 static VALUE rb_llama_llama_backend_free(VALUE self) { llama_backend_free(); return Qnil; } |
.backend_init(*args) ⇒ Object
module functions
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# File 'ext/llama_cpp/llama_cpp.cpp', line 2443
static VALUE rb_llama_llama_backend_init(int argc, VALUE* argv, VALUE self) {
VALUE kw_args = Qnil;
ID kw_table[1] = { rb_intern("numa") };
VALUE kw_values[1] = { Qundef };
rb_scan_args(argc, argv, ":", &kw_args);
rb_get_kwargs(kw_args, kw_table, 0, 1, kw_values);
const bool numa = kw_values[0] == Qundef ? false : (RTEST(kw_values[0]) ? true : false);
llama_backend_init(numa);
return Qnil;
}
|
.generate(context, prompt, n_predict: 128, n_threads: 1, n_keep: 10, n_batch: 512, repeat_last_n: 64, repeat_penalty: 1.1, frequency: 0.0, presence: 0.0, top_k: 40, top_p: 0.95, tfs_z: 1.0, typical_p: 1.0, temperature: 0.8) ⇒ String
Generates sentences following the given prompt for operation check.
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# File 'lib/llama_cpp.rb', line 31 def generate(context, prompt, # rubocop:disable Metrics/AbcSize, Metrics/CyclomaticComplexity, Metrics/MethodLength, Metrics/ParameterLists, Metrics/PerceivedComplexity n_predict: 128, n_threads: 1, n_keep: 10, n_batch: 512, repeat_last_n: 64, repeat_penalty: 1.1, frequency: 0.0, presence: 0.0, top_k: 40, top_p: 0.95, tfs_z: 1.0, typical_p: 1.0, temperature: 0.8) raise ArgumentError, 'context must be an instance of LLaMACpp::Context' unless context.is_a?(LLaMACpp::Context) raise ArgumentError, 'prompt must be a String' unless prompt.is_a?(String) spaced_prompt = " #{prompt}" embd_input = context.tokenize(text: spaced_prompt, add_bos: true) n_ctx = context.n_ctx raise ArgumentError, "prompt is too long #{embd_input.size} tokens, maximum is #{n_ctx - 4}" if embd_input.size > n_ctx - 4 last_n_tokens = [0] * n_ctx embd = [] n_consumed = 0 n_past = 0 n_remain = n_predict n_vocab = context.n_vocab output = [] while n_remain != 0 unless embd.empty? if n_past + embd.size > n_ctx n_left = n_past - n_keep n_past = n_keep embd.insert(0, last_n_tokens[(n_ctx - (n_left / 2) - embd.size)...-embd.size]) end context.eval(tokens: embd, n_past: n_past, n_threads: n_threads) end n_past += embd.size embd.clear if embd_input.size <= n_consumed logits = context.logits base_candidates = Array.new(n_vocab) { |i| LLaMACpp::TokenData.new(id: i, logit: logits[i], p: 0.0) } candidates = LLaMACpp::TokenDataArray.new(base_candidates) # apply penalties last_n_repeat = [last_n_tokens.size, repeat_last_n, n_ctx].min context.sample_repetition_penalty(candidates, last_n_tokens[-last_n_repeat..], penalty: repeat_penalty) context.sample_frequency_and_presence_penalties( candidates, last_n_tokens[-last_n_repeat..], frequency: frequency, presence: presence ) # temperature sampling context.sample_top_k(candidates, k: top_k) context.sample_tail_free(candidates, z: tfs_z) context.sample_typical(candidates, prob: typical_p) context.sample_top_p(candidates, prob: top_p) context.sample_temperature(candidates, temperature: temperature) id = context.sample_token(candidates) last_n_tokens.shift last_n_tokens.push(id) embd.push(id) n_remain -= 1 else while embd_input.size > n_consumed embd.push(embd_input[n_consumed]) last_n_tokens.shift last_n_tokens.push(embd_input[n_consumed]) n_consumed += 1 break if embd.size >= n_batch end end embd.each { |token| output << context.token_to_str(token) } break if !embd.empty? && embd[-1] == context.token_eos end output.join.delete_prefix(spaced_prompt).strip end |
.max_devices ⇒ Object
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# File 'ext/llama_cpp/llama_cpp.cpp', line 2511 static VALUE rb_llama_max_devices(VALUE self) { return INT2NUM(llama_max_devices()); } |
.mlock_supported? ⇒ Boolean
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# File 'ext/llama_cpp/llama_cpp.cpp', line 2507 static VALUE rb_llama_mlock_supported(VALUE self) { return llama_mlock_supported() ? Qtrue : Qfalse; } |
.mmap_supported? ⇒ Boolean
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# File 'ext/llama_cpp/llama_cpp.cpp', line 2503 static VALUE rb_llama_mmap_supported(VALUE self) { return llama_mmap_supported() ? Qtrue : Qfalse; } |
.model_quantize(*args) ⇒ Object
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# File 'ext/llama_cpp/llama_cpp.cpp', line 2462
static VALUE rb_llama_model_quantize(int argc, VALUE* argv, VALUE self) {
VALUE kw_args = Qnil;
ID kw_table[3] = { rb_intern("input_path"), rb_intern("output_path"), rb_intern("params") };
VALUE kw_values[3] = { Qundef, Qundef, Qundef };
rb_scan_args(argc, argv, ":", &kw_args);
rb_get_kwargs(kw_args, kw_table, 3, 0, kw_values);
if (!RB_TYPE_P(kw_values[0], T_STRING)) {
rb_raise(rb_eArgError, "input_path must be a string");
return Qnil;
}
if (!RB_TYPE_P(kw_values[1], T_STRING)) {
rb_raise(rb_eArgError, "output_path must be a string");
return Qnil;
}
if (!rb_obj_is_kind_of(kw_values[2], rb_cLLaMAModelQuantizeParams)) {
rb_raise(rb_eArgError, "params must be a ModelQuantizeParams");
return Qnil;
}
const char* input_path = StringValueCStr(kw_values[0]);
const char* output_path = StringValueCStr(kw_values[1]);
LLaMAModelQuantizeParamsWrapper* wrapper = RbLLaMAModelQuantizeParams::get_llama_model_quantize_params(kw_values[2]);
if (llama_model_quantize(input_path, output_path, &(wrapper->params)) != 0) {
rb_raise(rb_eRuntimeError, "Failed to quantize model");
return Qnil;
}
return Qnil;
}
|
.print_system_info ⇒ Object
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# File 'ext/llama_cpp/llama_cpp.cpp', line 2494 static VALUE rb_llama_print_system_info(VALUE self) { const char* result = llama_print_system_info(); return rb_utf8_str_new_cstr(result); } |
.time_us ⇒ Object
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# File 'ext/llama_cpp/llama_cpp.cpp', line 2499 static VALUE rb_llama_time_us(VALUE self) { return LONG2NUM(llama_time_us()); } |